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1. 0000 5 66 5 3 Documents Content plus Annotations plus Features 66 5 4 Annotations Directed Acyclic Graphs o o 66 5 4 1 Annotation Schemas y DA A ee as 66 5 4 2 Examples of Annotated Documents 68 5 4 3 Viewing and Editing Diverse Annotation Types 70 5 5 Document Formats lt 200 8 a a te o be BE Eh oe ett 71 5 5 1 Detecting the right reader lt lt cita asia e 426 3 e 4 ee Ses 72 wA AA poe Ha nen cl wight oh wh Uh aaa eng nis eid weeds Bh 74 I5 NARA ge A a A esas ae A Wy ate aR ae es Ge A a acca ae 80 gA SGM ves ee a tee OA Re be oe aide Bee ald we Sa Sh 81 5 07 Plaintext oo ea Ste Meck A AA AA ee A ee 81 O REF ek ah sire mde ie kh a Ste eee o Boe 82 SoG A A Gear oe Cs nes See ge ta e ae oa en gad Bec Sh ah 83 5 6 XML Tipu OU BUG otf ott As ale dh te ad de a dt lls ah des 84 6 JAPE Regular Expressions Over Annotations 86 Gils Wise ot Context wiret e Rd RA ee Sie Se a er ee 90 072 Use ot BOTES a la Sie e ot elena ei at eo eae SS a y eg a ee 91 Gis USCIS er had we Se as a So een Say Oe ee eee Et 93 6 4 Using Java code in JAPE rules o 95 6 4 1 Adding a feature to the document o 97 6 5 Optimising for speed 2 9 a e Bl ee Oe eee eee ERR oS 97 7 ANNIE a Nearly New Information Extraction System 99 GE VokeMmisers a ee Bod yee el age eal dh ee aai 100 Gell Tokentser Rules a A SUG A RS
2. 2 15 2 Unrestricted Annotation Editor o o lv CO ONN OOK WN ALLAN OLEO NEU EU 2 15 3 Saving the test data 2a ea oP yo a eG re 2 16 D Save and Restore LRs in Data Stores 2 o o 2 17 D Save Resource Parameter State to File 0 2 18 D F Perform Evaluation with the AnnotationDiff tool 2 18 1 GUD e 2 am AA AS Ge ae A Ng Shag DAS AL A A A a A AS it ES a Rs 2 18 3 Annotation Diff parameters o e 2 18 4 Reading the results from the Annotation Diff 2 19 D Use the Corpus Benchmark Evaluation tool 2 19 1 2 19 2 2 19 3 GUrm de eon SB cake Sanh a acdc te A A a fh Bek aia ee gaa a a Standalone mode 2 Lee dae fb RES be be ee woh ee A How to define the properties of the benchmark tool 2 20 D Write JAPE Grammars 24 34 04 004 os bd aa aed 2 21 F Embed NLE in other Applications o o o 2 22 D F Add support for a new document format 2 23 D F Create a New Annotation Schema o 2 24 D Dump Results to File Sin Vd a daa dr ad Bed 2 25 D Stop GUI Freezing on Linux a A 2 26 D Stop GUI Crashing on Linux dea aa E Sy tees 2 27 D Stop GATE Restoring GUI Sessions Options 2 28 Work With Unicode s cos dos eiu aa a Sede Bat oe he Bod Bee 2 29 Work With Oracles a s sateni atte nee a E Soe aad Sn CREOL
3. Well it was a kind of back to front program It s funny how many of the best ideas are just an old idea back to front You see there have already been several programs written that help you to arrive at decisions by properly ordering and analysing all the relevant facts so that they then point naturally towards the right decision The drawback with these is that the decision which all the properly ordered and analysed facts point to is not necessarily the one you want Yeeeess said Reg s voice from the kitchen Well Gordon s great insight was to design a program which allowed you to specify in advance what decision you wished it to reach and only then to give it all the facts The program s task which it was able to accomplish with consum mate ease was simply to construct a plausible series of logical sounding steps to connect the premises with the conclusion And I have to say that it worked brilliantly Gordon was able to buy himself a Porsche almost immediately despite being completely broke and a hopeless driver Even his bank manager was unable to find fault with his reasoning Even when Gordon wrote it off three weeks later Heavens And did the program sell very well No We never sold a single copy You astonish me It sounds like a real winner to me It was said Richard hesitantly The entire project was bought up lock stock and barrel by the Pentagon The
4. million b billion Since none of these exist in the text it passes onto the next macro The AMOUNT_NUMBER macro recognises a number optionally followed by any number of sequences of the form dot or comma plus number followed SS SE O OO UNES EE Se A ee SA ee LLO by an optional space and an optional MILLION_BILLION In this case 800 000 will be recognised Finally the rule Moneyl is invoked This recognises the string identified by the AMOUNT_NUMBER macro followed by an optional space followed by a unit of currency as determined by the gazetteer In this case US dollars has been identified as a currency unit so the rule Moneyl recognises the entire string 800 000 US dollars Following the rule it will be annotated as a Number entity of type Money Number kind money rule Moneyl Chapter 8 More CREOLE For the previous reader was none other than myself I had already read this book long ago The old sickness has me in its grip again amnesia in litteris the total loss of literary memory I am overcome by a wave of resignation at the vanity of all striving for knowledge all striving of any kind Why read at all Why read this book a second time since I know that very soon not even a shadow of a recollection will remain of it Why do anything at all when all things fall apart Why live when one must die And I clap the lovely book shut stand up and slink back vanquished demol
5. gate AnnotationSet person gate AnnotationSet bindings get person gate Annotation personAnn gate Annotation person iterator next gate FeatureMap features Factory newFeatureMap features put gender personAnn getFeatures get minorType features put rule FirstName outputAS add person firstNode person lastNode FirstPerson features The second rule contained in a subsequent grammar phase makes use of annotations pro duced by the first rule described above Instead of percolating the minorType from the annotation produced by the gazetteer lookup this time it percolates the feature from the annotation produced by the previous grammar rule So here it gets the gender feature value from the FirstPerson annotation and adds it to a new feature again called gender for convenience which is added to the new annotation in outputAS TempPerson At the end of this rule the existing input annotations from inputAS are removed because they are no longer needed Note that in the previous rule the existing annotations were not removed because it is possible they might be needed later on in another grammar phase Rule GazPersonFirst FirstPerson person iw f MEM PATO Seto pe fil i a MMA a os A VY LULE NAL RE SES ve gt gate AnnotationSet person gate AnnotationSet bindings get person gate Annotation personAnn gate Annotation pe
6. Hepple 00a M Hepple Independence and Commitment Assumptions for Rapid Training and Execution of Rule based Part of Speech Taggers In Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics Hong Kong October 2000 Hepple 00b M Hepple Independence and commitment Assumptions for rapid training and execution of rule based POS taggers In Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics ACL 2000 Hong Kong October 2000 Humphreys et al 96 K Humphreys R Gaizauskas H Cunningham and S Azzam CREOLE Module Specifications http www dcs shef ac uk nlp gate 1996 Jackson 75 M Jackson Principles of Program Design Academic Press London 1975 LREC 1 98 Conference on Language Resources Evaluation LREC 1 Granada Spain 1998 LREC 2 00 Second Conference on Language Resources Evaluation LREC 2 Athens 2000 Manning amp Schiitze 99 C Manning and H Schiitze Foundations of Statistical Natural Language Pro cessing MIT press Cambridge MA 1999 Supporting materials available at http www sultry arts usyd edu au fsnlp Maynard et al 00 D Maynard H Cunningham K Bontcheva R Catizone G Demetriou R Gaizauskas O Hamza M Hepple P Herring B Mitchell M Oakes W Peters A Setzer M Stevenson V Tablan C Ursu and Y Wilks A Survey of Uses of GATE Technical Report CS 00 06 Department of Computer Science
7. annotDiff getAnnotationsOfType AnnotationDiff CORRECT_TYPE AnnotationSet partiallyCorrectAnnot annotDiff getAnnotationsOfType AnnotationDiff PARTIALLY_CORRECT_TYPE AnnotationSet missingAnnot annotDiff getAnnotationsOfType AnnotationDiff MISSING_TYPE AnnotationSet spuriousAnnot annotDiff getAnnotationsOfType AnnotationDiff SPURIOUS_TYPE SS SEE O OO E UNES EE Se A ee ee SS ee 2 19 Dj Use the Corpus Benchmark Evaluation tool The Corpus Benchmark tool can be run in two ways standalone and GUI mode Section 9 3 describes the theory behind this tool 2 19 1 GUI mode To use the tool in GUI mode select Corpus Benchmark Tool from the Options menu Select Default mode to compare the stored processed set with the current processed set and the human annotated set Select human marked against stored processing results to compare the stored processed set with the human annotated set Select human marked against current processing results to compare the current processed set with the human annotated set Once the mode has been selected choose the directory where the corpus is to be found The corpus must have a directory structure consisting of clean and marked subdirectories The clean directory should contain the raw texts the marked directory shuold contain the human annotated texts If the tool is to be used in Default or Current mode the corpus must first be processed wi
8. to practical instructions for programmers e g how to deal with GATE ex ceptions and linguists e g how to write a pattern grammar This diversity is something of an organisational challenge Our no doubt imperfect solution is to collect specific in structions for how to do X in a separate chapter 2 Other chapters give a more discursive presentation In order to understand the whole system you must unfortunately read much Languages other than Java require an additional interface layer such as JNI the Java Native Interface which is in C l0nttp gate ac uk GateExamples doc java2htm1 sheffield examples StandAloneAnnie java html TAREA O TAREA O UNES EE Se A ee ee SS ee of the book in order to get help with a particular task however look first in chapter 2 and refer to other material as necessary The other chapters Chapter 3 describes the GATE architecture s component based model of language processing describes the lifecycle of GATE components and how they can be grouped into applications and stored in databases and files Chapter 4 describes the set of Visual Resources that are bundled with GATE Chapter 5 describes GATE s model of document formats annotated documents annotation types and corpora sets of documents It also covers GATE s facilities for reading and writing in the XML data interchange language Chapter 6 describes JAPE a pattern action rule language based on regular expressions over
9. A l Patterns GATE is structured around a number of what we might call principles or patterns or alternatively clever ideas stolen from better minds than mine These patterns are 134 do See ee ivy e modelling most things as extensible sets of components cf Section A 1 1 e separating components into model view or controller cf Section A 1 2 types e hiding implementation behind interfaces cf Section A 1 3 Four interfaces in the top level package describe the GATE view of components Resource ProcessingResource LanguageResource and VisualResource A 1 1 Components Architectural Principle Wherever users of the architecture may wish to extend the set of a particular type of entity those types should be expressed as components Another way to express this is to say that the architecture is based on agents lve avoided this in the past because of an association between this term and the idea of bits of code moving around between machines of their own volition I take this to be somewhat pointless and probably the result of an anthropomorphic obsession with mobility as a correlate of intelligence If we drop this connotation however we can say that GATE is an agent based architecture If we want to that is Framework Expression Many of the classes in the framework are components by which we mean classes that conform to an interface with certain standard properties In our case these properties are based on the Ja
10. Figure 2 4 Adding an Organization annotation to the Default Annotation Set Mr Clever and then click on the desired annotation type e g Person which is shown in the list of types on the right hand side of the document viewer If however the desired annotation type does not already appear there or the user wants to associate more detailed information with the annotation not just its type then an annotation editing dialogue can be used Figure 2 4 demonstrates adding the Organization annotation for the string University of Sheffield highlighted in grey to the Default Annotation set 2 15 1 Schema Annotation Editor To use the Schema Annotation Editor select the text to be annotated right click and select the annotation set to which the annotation should be added An Edit Annotation popup window should appear offering the choice between a Schema Annotation Editor the default and an Unrestricted Annotation Editor If an annotation schema already exists for an annotation type the Schema Annotation Editor can be used to add a new annotation to a document For information about annotation iw f MEM PATO Seto e fia A Se NNO a os A VY LULE NAL RE SES ve schemas see Section 5 4 1 To see whether an annotation schema has been loaded it will be present in the dropdown list of annotation types in the Schema Annotation Editor If the relevant annotation schema does not exist it must either be created and or loaded se
11. Paragraph 1 This text belongs to the first paragraph Paragraph 2 This text belongs to the second paragraph then two paragraph type annotation will be created in the Original markups annotation set refereing the first and second paragraphs with an empty feature map The extensions associate with the plain text reader are e txt e text The web server content type associate with plain text documents is text plain There is no magic numbers test for plain text Output When attempting to preserve the original markup formatting GATE will dump XML markup that surrounds the text refereed The procedure described above applies both for plain text and RTF documents 5 5 6 RTF Input Accessing RTF documents is performed by using the Java s RTF editor kit It only extracts the document s text content from the RTF document The extension associate with the RTF reader is rtf The web server content type associate with xml documents is text rtf The magic numbers test searches for rtf1 aS SEE O SO E UNES AE ve eee AE SSS IS Output Same as the plain tex output 5 5 7 Email Input GATE is able to read email messages packed in one document UNIX mailbox format It detects multiple messages inside such documents and for each message it creates annotations for all the fields composing an e mail like date from to subject etc The message s body is analyzed and a paragraph detection is perf
12. e Language Resources modelling Documents and Corpora and various types of Annota tion Schema see chapter 5 e Processing Resources that are part of the ANNIE system see chapter 7 e Visual Resources for viewing and editing corpora annotations etc e Other miscellaneous resources see chapter 8 Contributions to further developments gratefully received unmarked low denomination notes preferred Bugs to santa northpole org Chapter 4 Visual CREOLE neurobiologists still go on openly studying reflexes and looking under the hood not huddling passively in the trenches Many of them still keep wondering how does the inner life arise Ever puzzled they oscillate between two major fictions 1 The brain can be understood 2 We will never come close Meanwhile they keep pursuing brain mechanisms partly from habit partly out of faith Their premise The brain is the organ of the mind Clearly this three pound lump of tissue is the source of our insight information about our very being Somewhere in it there might be a few hidden guidelines for better ways to lead our lives Zen and the Brain James H Austin 1998 p 6 This chapter details the other visual resources that are bundled with GATE 4 1 Ontogazetteer The Ontogazetteer or Hierarchical Gazetteer is an interface which makes ontologies visible in GATE supporting basic methods for hierarchy management and traversal In GATE an ontology is re
13. e accessor and mutator methods for data members are named after those members plus get and set meaning that the tool can figure out how to use a member or property of a bean from information provided by Java reflection e beans must have no argument constructors so that tools can construct instances of beans without knowing about complex initialisation parameters The rest of this section says a little more about the Beans specification skip to the next if you re only interested in how it works in GATE Quoting from Campione et al 98 at Sun s Java website The JavaBeans API makes it possible to write component software in the Java programming language Components are self contained reusable software units that can be visually composed into composite components applets applications and servlets using visual application builder tools JavaBean components are known as Beans In this context we may think of the GATE development environment as a builder tool While the emphasis in the quoted text is on visual representation of components note that GATE and other beans can also be plugged together invisibly this is what the framework does and how GATE beans are typically deployed into other applications Components expose their features for example public methods and events to builder tools for visual manipulation A Bean s features are exposed because feature names adhere to specific design patterns A Java
14. is used instead of just component because it is a common term in the literature of the field cf the Language Resources and Evaluation conference series LREC 1 98 LREC 2 00 a atlanta HPA do Aai A a AAA mh email VY LULE NAL RE SES Y such as grammars that are used by existing Processing Resources or a mixture of both The development environment is used for visualisation of the data structures produced and consumed during processing and for debugging performance measurement and so on For example figure 1 1 is a screenshot of one of the visualisation tools displaying named entity Gate 2 Dalpha2 build 499 E A ali A Shion Gate Messages E BengaliSampleTextutf8 ba 8 Applications Bengali NE B Language Resources 3 BengaliSampleT ext utf8 t Processing Resources 8 BengaliNE A BengaliT okeniser 3 bengali_gazetteer e Data stores My name is Anil Roy live in Lancaster My father lives in Liverpool My father s name is Rajesh Roy Lancaster University is my place of e Bj Type Ser Starts Features End Person Default 10 18 kind fullname Location Default 27 38 kind city rule City Location Defaut 59 67 kind chty rule City Person Defauit 101 112 kind fullname Organisation Default 115 141 la Defaut 173 19219 Organisation Bengali NE run in 0
15. or from your UNIX supplier We test on various Sun 1 3 and 1 4 JDKs on Solaris Linux NT 4 Windoze 2000 and Windoze XP e Binaries from the GATE distribution you downloaded gate jar guk jar Unicode editing support and a suitable script to start Java e g gate sh or gate bat These are held in a directory called bin like this bin gate jar gate sh gate bat bin ext guk jar e An open mind and a sense of humour Using the binary distribution 2http www zerog com 3http java sun com products jdk LN FAME SA a e pe fil A fe MEA a os A VY LULE NAL RE SES Unpack the distribution creating a directory containing jar files and scripts If you want to copy the scripts that run the system somewhere else then you need to set some environment variables see below To run the development environment on Windows click on gate bat on UNIX run gate sh To embed GATE as a library put gate jar in your CLASSPATH and tell Java that guk jar is an extension Djava ext dirs path to guk jar The scripts that start GATE gate bat or gate sh use three environment variables 1 JAVA_HOME should point to the location of java if it is not set the scripts assume that it is in your PATH Note the ZeroG installer sets this variable for you in the startup script GATE_HOME should point to the directory containing the binaries directory bin which contains gate jar and the ext directory containing gu
16. s content will be as follows This is a titleThis is a paragraphHere is an useful link The annotations created will refer the right parts of the texts but for the GATE s processing resources like tokenizer gazetter etc which work on this text this will be a major diaster Therefore in order to prevent this problem from happening GATE checks if it s likely to join words and if this happens then it inserts a space between those words So the text will look like this after loaded in GATE This is a title This is a paragraph Here is an useful link There are cases when these words are meant to be joined but they are just a few This is why it s an open problem iw f MEM PATO Seto pe fil i a MMA De en aos nny VY LULE NAL RE SES ov The extensions associate with the XML reader are e xml e xhtm e xhtml The web server content type associate with xml documents is text zmi The magic numbers test searches inside the document for the XML lt xml version 1 0 signature It is also able to detect if the XML document uses the semantic described in the GATE document format DTD see section 5 5 2 or uses other semantics Output GATE is capable to assure persistence for its resources These layers of persistence are various and they span until database persistence However for some purposes a light and simple level of persistence would be highly appreciated The types of persistent storage used for Language Resources ar
17. s component model Java Beans section 3 3 describes GATE s extended Beans model 52 TAREA O AEREO UNES ES AE A eR eS ee GATE components may be implemented by a variety of programming languages and databases but in each case they are represented to the system as a Java class This class may do nothing other than call the underlying program or provide an access layer to a database on the other hand it may implement the whole component GATE components are one of three types e LanguageResources LRs represent entities such as lexicons corpora or ontologies e ProcessingResources PRs represent entities that are primarily algorithmic such as parsers generators or ngram modellers e VisualResources VRs represent visualisation and editing components that participate in GUIs Section 3 4 discusses the disctinction between Language Resources and Processing Resources Collectively the set of resources integrated with GATE is known as CREOLE a Collection of REusable Objects for Language Engineering In the rest of this chapter e section 3 5 describes the lifecycle of GATE components e section 3 6 describes how Processing Resources can be grouped into applications e section 3 7 describes the relationship between Language Resources and their data stores e section 3 8 summarises GATE s set of built in components 3 1 The Web and CREOLE GATE allows resource implementations and Language Resource persistent data t
18. ConsumeBlock Java code Figure B 1 BNF of JAPE s grammar PE PRY E Ia Cae are tee ae ee ae en S itv An example of a complete rule Rule NumbersAndUnit Token kind number numbers Token kind unit gt numbers Name 4 rule NumbersAndUnit This says match sequences of numbers followed by a unit create a Name annotation across the span of the numbers and attribute rule with value NumbersAndUnit B 2 Relation to CPSL We differ from the CPSL spec in various ways 1 No pre or post fix context is allowed on the LHS 2 No function calls on the LHS 3 No string shorthand on the LHS 4 We have two rule application algorithms one like TextPro one like Brill Mitre See section B 3 5 Expressions relating to labels unbound on the LHS are not evaluated on the RHS In TextPro they evaluate to false See the binding scheme description in section B 4 6 JAPE allows arbitrary Java code on the RHS 7 JAPE has a different macro syntax and allows macros for both the RHS and LHS 8 JAPE grammars are compiled and stored as serialised Java objects Apart from this it is a full implementation of CPSL and the formal power of the languages is the same except that a JAPE RHS can delete annotations which straight CPSL cannot The rule LHS is a regular language over annotations the rule RHS can perform arbitrary transformations on annotations but the RHS is only fired after the L
19. H Cunningham K Bontcheva V Tablan and Y Wilks Software Infrastructure for Language Resources a Taxonomy of Previous Work and a Requirements Analy sis In Proceedings of the 2nd International Conference on Language Resources and Evaluation LREC 2 Athens 2000 http gate ac uk Cunningham et al 00c H Cunningham D Maynard K Bontcheva V Tablan and Y Wilks Experience of using GATE for NLP R amp D In Proceedings of the Workshop on Using Toolsets A LLAVE NANO NS ive and Architectures To Build NLP Systems at COLING 2000 Luxembourg 2000 http gate ac uk Cunningham et al 00d H Cunningham D Maynard and V Tablan JAPE a Java Annotation Patterns Engine Second Edition Research Memorandum CS 00 10 Department of Com puter Science University of Sheffield November 2000 Cunningham et al 02 H Cunningham D Maynard K Bontcheva and V Tablan GATE A framework and graphical development environment for robust NLP tools and applications In Proceedings of the 40th Anniversary Meeting of the Association for Computational Linguistics 2002 Dimitrov 02 M Dimitrov A Light weight Approach to Coreference Resolution for Named Entities in Text MSc Thesis University of Sofia Bulgaria 2002 http www ontotext com ie thesis m pdf Dimitrov et al 02 M Dimitrov K Bontcheva H Cunningham and D Maynard A Light weight Approach to Coreference Resolution for Named Entities in Text In Proceedin
20. a path in the graph above can be 1 2 4 7 8 10 Note that the next step continues from the rightmost node reached by the annotations in the current step The matchings are made by a Finite State Machine that resembles an clasical lexical analyser aka scanner The main difference from a scanner is that there are no input symbols the transition from one state to another is based on matching a set of objects annotations against a set of restrictions the constraint group in the LHS of a CPSL rule The algorithm can be the following 1 startNode the leftmost node PE PRY E Sa Cae ae ee tee ae ee ae en S ite 2 create a first instance of the FSM and add it to the list of active instances 3 for this FSM instance set current node as the leftmost node 4 while startNode last node do 1 while not over do 1 for each F i active instance of the FSM do 1 if this instance is in a final state then save a clone of it in the set of accepting FSMs instances of the FSM that have reached a final state 2 read all the annotations starting from the current node 3 select all sets of annotation that can be used to advance one step in the transition graph of the FSM 4 for each such set create a new instance of the FSM put it in the active list and make it consume the corresponding set of annotations making any necessary bindings in the process this new instance will advance in the annotation graph to the rightmost node that
21. lt enumeration value url gt lt enumeration value phone gt lt enumeration value ip gt ve SS SE a e ee e ara o aar o Se ee ee ee a Se e lt enumeration value street gt lt enumeration value postcode gt lt enumeration value country gt lt enumeration value complete gt lt restriction gt lt simpleType gt lt attribute gt lt complexType gt lt element gt lt schema gt 5 4 2 Examples of Annotated Documents This section shows some simple examples of annotated documents This material is adapted from Grishman 97 the TIPSTER Architecture Design document upon which GATE version 1 was based Version 2 has a similar model although annotations are now graphs and instead of multiple spans per annotation each annotation now has a sin gle start end node pair The current model is largely compatible with Bird amp Liberman 99 and roughly isomorphic with stand off markup as latterly adopted by the SGML XML community Each example is shown in the form of a table At the top of the table is the document being annotated immediately below the line with the document is a ruler showing the position byte offset of each character NOTE the ruler doesn t scale very well in HTML for a better picture see the original TIPSTER Architecture Design Document Underneath this appear the annotations one annotation per line For each annotation is shown its Id Type Span start end offsets derived
22. method needs to be called to make it calculate the statistics Example AnnotationDiff annotDiff new AnnotationDiff annotDiff setKeyDocument keyDocument annotDiff setResponseDocument responseDocument annotDiff setAnnotationSchema annotationSchema annotDiff setKeyAnnotationSetName GateConstants ORIGINAL_MARKUPS_ANNOT_SET_NAME annotDiff setResponseAnnotationSetName GateConstants ORIGINAL_MARKUPS_ANNOT_SET_NAME annotDiff init It is important to know that its initQ method performs the diff between two sets of annotations So after the initialization one could use the getZZZ methods to read the results of the evaluation 2 18 3 Annotation Diff parameters All Annotation Diff parameters by default are initialized to null except textMode and they are as follows keyDocument the document holding the key annotation sets that will be used in the evaluation It is a gate Document and if it is null then init will throw a Resourcelnstanti ationException responseDocument the document processed by GATE containing annotation sets gen erated by the system AnnotationDiff will behave the same as the keyDocument if this parameter it is set to null annotationSchema It represent an gate creole AnnotationSchema object that describes an annotation The information used by the Annotation Diff is the type of the annotations being evaluated keyAnnotationSetName a String representing the name of th
23. particular observation to be made for the HTML formats When attempting to preserve the original markup formatting GATE will generate the doc ument in xhtml The html document will look the same with any browser after processed by GATE but it will be in another syntax 5 5 4 SGML Input The SGML support in GATE is fairly light as there is no freely available Java SGML parser GATE uses a light converter attempting to transform the input SGML file into a well formed XML Because it does not make use of a DTD the conversion might not be always good It is advisable to perform a SGML2XML conversion outside the system using some other specialized tools before using the SGML document inside GATE The extensions associate with the SGML reader are e sgm e sgml The web server content type associate with xml documents is text sgml There is no magic numbers test for SGML Output When attempting to preserve the original markup formatting GATE will generate the doc ument as XML because the real input of a SGML document inside GATE is an XML one 5 5 5 Plain text Input When reading a plain text document GATE attempts to detect its paragraphs and add paragraph annotations to the document s Original markups annotation set It does SS SE a e a e e Se a ee ee RE SS ee that by detecting two consecutive NLs The procedure works for both UNIX like or DOS like text files Example If the plain text read is as follows
24. the anno tations which were consumed during the match are available from that element as are a composite span set and a single span that covers the whole set In general these will only be accessed via a bindingName which is associated with ComplexPatternElements The LeftHandSide maintains a mapping of bindingNames to ComplexPatternElements which are accessed by array reference in Rule RightHandSides Although PatternElements give access to an annotation set these are only built when they are asked for caching ensures that they are only built once to avoid storing annotations against every matched element When asked for the construction process is an iterative traversal of the elements contained within the element being asked for the annotations This traversal always bottoms out into BasicPatternElements which are the only ones that need to store annotations all the time In a RightHandSide application then a call to the LeftHandSide s binding environment will yield a ComplexPatternElement representing the bound object from which annotations and spans can be retrieved as needed ERAS E SIS Cee ae tee ae ee ae en S iVe B 6 2 Example RHS code Let s imagine we are writing an RHS for a rule which binds a set of annotations representing simple numbers to the label numbers We want to create a new annotation spanning all the ones matched whose value is an Integer representing the sum of the individual numbers The RHS cons
25. ules but can be loaded separately as a Processing Resource T he main module consists of three submodules e quoted text module e pleonastic it module e pronominal resolution module The first two modules are helper submodules for the pronominal one because they do not perform anything related to coreference resolution except the location of quoted fragments and pleonastic it occurrences in text They generate temporary annotations which are used by the pronominal submodule such temporary annotations are removed later iw f MEM PATO Seto pe fil i a MMA ES OM ee nee VY LULE NAL RE SES Uv The main coreference module can operate successfully only if all ANNIE modules were already executed The module depends on the following annotations created from the re spective ANNIE modules Token English Tokenizer e Sentence Sentence Splitter Split Sentence Splitter e Location NE Transducer OrthoMatcher e Person NE Transducer OrthoMatcher Organization NE Transducer OrthoMatcher For each pronoun anaphor the coreference module generates an annotation of type Coref erence containing two features e antecedent offset this is the offset of the starting node for the annotation entity which is proposed as the antecedent or null if no antecedent can be proposed e matches this is a list of annotation IDs that comprise the coreference chain comprising this anaphor antecedent pair 7 7 1 Quoted Speech Submod
26. 1 1 16 1999 Cunningham 99b H Cunningham Information Extraction a User Guide revised version Research Memorandum CS 99 07 Department of Computer Science University of Sheffield May 1999 Cunningham 99c H Cunningham JAPE a Java Annotation Patterns Engine Research Memorandum CS 99 06 Department of Computer Science University of Sheffield May 1999 A LLAVE NANO NS ivy Cunningham 00 H Cunningham Software Architecture for Language Engineering Unpublished PhD thesis University of Sheffield 2000 http gate ac uk sale thesis Cunningham 02 H Cunningham GATE a General Architecture for Text Engineering Computers and the Humanities 36 223 254 2002 Cunningham et al 94 H Cunningham M Freeman and W Black Software Reuse Object Oriented Frameworks and Natural Language Processing In New Methods in Language Pro cessing NeMLaP 1 September 1994 Manchester 1994 Re published in book form 1997 by UCL Press Cunningham et al 95 H Cunningham R Gaizauskas and Y Wilks A General Architecture for Text Engineering GATE a new approach to Language Engineering R amp D Technical Report CS 95 21 Department of Computer Science University of Sheffield 1995 http xxx lanl gov abs cs CL 9601009 Cunningham et al 96a H Cunningham K Humphreys R Gaizauskas and M Stower CREOLE Devel oper s Manual Technical report Department of Computer Science University of Sheffield 1996 ht
27. 140 145 150 d Type SpanStart Span End Features PA E Al 5 Priority 7 Table 5 3 Annotation showing overall document structure At a less esoteric level annotations can be used to record the overall structure of documents including in particular documents which have structured headers as is shown in the third example Table 5 3 If the Addressee Source annotations are recorded when the document is indexed for retrieval it will be possible to perform retrieval selectively on information in particular fields Our final example Table 5 4 involves an annotation which effectively modifies the document The current architecture does not make any specific provision for the modification of the original text However some allowance must be made for processes such as spelling correction This information will be recorded as a correction feature on token annotations and possibly on name annotations 5 4 3 Viewing and Editing Diverse Annotation Types To view and edit annotation types see Section 2 13 To add annotations of a new type see Section 2 15 To add a new annotation schema see Section 2 23 Lv HEEE ALO a e eo A NEO a oe See io VY ELULE NAL RE SES ei 10 15 20 25 z pos NP correciion TIPSTER wo fps SSS 2 pos NNS correction terabytes EU T ioken 2 token 8 3 token e a token 8 Table 5 4 Annotation modifying the document 5 5 Document Formats The following document formats a
28. 3 The GATE Framework 4 st 2 408 o O ds ado oun Fk wh ALLAN OLEO NEU EU 3 4 Language Resources and Processing Resources 3 5 The Lifecycle of a CREOLE Resource 0 0 00048 3 6 Processing Resources and Applications 4 3 7 Language Resources and Datastores o a 0000004 3 8 Built in CREOLE Resources o e 000 eee eee Visual CREOLE 4T A dow one BASS EAM Se SA 4 2 Prot g in GATE 6 ee Ee BEES ad EI OS eR Ee as Corpora Documents and Annotations 5 1 Features Simple Attribute Value Data 5 2 Corpora Sets of Documents plus Features 5 3 Documents Content plus Annotations plus Features 5 4 Annotations Directed Acyclic Graphs 0 o 5 5 Document Formats y esa pa AAA A 56 AM Inp it Output as ia ta a A do Dee ey Seed JAPE Regular Expressions Over Annotations 0 L Use oh Contes sr o a Bch ee PSS PETERS e 0 2 seo Priggity esa be Mb bee sd oe oe id eg Ga oes 637 Useful tricks 63 250 outs see hh at hi et ak a nek peg had ee eels eg 6 4 Using Java code in JAPE rules o 6 5 Optimising for speed leads e ede e oe a ANNIE a Nearly New Information Extraction System Fale TOKENISCI and Goa tet paa cad Daa ee a Yeo E Good ye eee a Wee GAZELEC Mi on Wate 9 Meee Heh R Ge aes Bede Sede Aide Be ean yee aes a od a Sentence Splitter aia m 1 48 ent oh gin oka Ga eS ee eG
29. 591 seconds Figure 1 1 One of GATE s visual resources extraction results for a Bengali sentence The GATE development environment is analogous to systems like Mathematica for Mathe maticians or JBuilder for Java programmers it provides a convenient graphical environment for research and development of language processing software When an appropriate set of resources have been developed they can then be embedded in the target client application using the GATE framework The framework is supplied as two JAR files To embed GATE based language processing facilities in an application these JAR files are all that is needed along with JAR files and XML configuration files for the various resources that make up the new facilities gt The main JAR file gate jar supplies the framework built in resources and various 3rd party libraries the second file guk jar the GATE Unicode Kit contains Unicode support e g additional input methods for languages not currently supported by the JDK They are separate because the latter has to be a Java extension with a privileged security profile iw t MEM PATO SSO pde fil i Se MMA a os A VY LULE NAL RE SES 1 3 2 Built in Components GATE includes resources for common LE data structures and algorithms including doc uments corpora and various annotation types a set of language analysis components for Information Extraction and a range of data visualisation and editing components GATE su
30. Ga He 101 leo Token Types 35 e s ri div ed dina Shee ss tea Bh 101 T2 Gazetteer AI a oe Gees ee ee he te ee 103 Go sentence Splitter u caw che da cee we aed te doe et fe bk fig 104 7 4 Part of Speech Tagger aimara a Big eet Gage eed Sater gee 104 7 5 Semantic Tagger A Wy sp Aso se ate tek oe geet es Ao at we ees tas 104 7 6 Orthographic Coreference OrthoMatcher o o o o o 104 7 6 1 GATE Interface A tte LM il aten Ad ida de did o Sia 105 120 2 REeSQUICES arste 3 lao 8 as a e a 105 10 3 PIOCESSIN Op e o aa a RA a 105 7 7 Pronominal Coreference a 105 7 7 1 Quoted Speech Submodule 106 7 7 2 Pleonastic It submodule o o 107 7 7 3 Pronominal Resolution Submodule o 107 7 7 4 Detailed description of the algorithm 107 7 8 A Walk Through Example e it A RAE ALA 111 ALLAN OLEO NEU EU Vis 7 8 1 Step 1 Tokenisation iaa a 112 192 Step be bist Lookup ida a A eee es er oe 112 7 8 3 Step 3 Grammar Rules 2 000 eee 112 8 More CREOLE 114 8 1 Document Restorant ed ead 115 8 2 Verb Group Ch nker sa uia u Faves A ges ee BO ese 115 8 3 Noun Group Chunker 3 ctro s gues e 24 aod dl 115 8 4 nto Text Gazeticers ep amp sake A A eB ae 117 8 4 1 Prerequisites sisi doble ed a ed a 117 84 2 Sep ak A A a ees a ee a 118 8 5 Flexible Exporter 3 2305 se does bow doen Se Me Ske ene dee a Ses 119 8 6 A
31. In the former case the XML output will be close to the original document In the latter case the format is a GATE specific one which can be read back by the system to recreate all the information that GATE held internally for the document In order to understand why there are two types of XML serialization one needs to understand the structure of a GATE document GATE allows a graph of annotations that refer to parts of the text Those annotations are grouped under annotation sets Because of this structure sometimes it is impossible to save a document as XML using tags that surround the text referred by the annotation because tags crossover situations could appear XML is essentially a tree based model of information whereas GATE uses graphs Therefore in order to preserve all annotations in a GATE document a custom type of XML document was developed The problem of crossover tags appears with GATE s second option the preserve format one which is implemented at the cost of loosing certain annotations The way it is applied in GATE is that it tries to restore the original markup and where it is possible to add in the same manner annotations produced by GATE How to access and make use of the two ways of XML serialization Save As XML option This option is available in GATE s GUI in the pop up menu associate with each language resource document or corpus Saving a corpus as XML is done by calling save as XML on each document of the
32. Java 2 3rd Edtn 1999 gives a slightly different view MVC separates applications into three types of objects e Models Maintain data and provide data accessor methods e Views Paint a visual representation of some or all of a model s data e Controllers Handle events By encapsulating what other architectures intertwine MVC applications are much more flexible and reusable than their traditional counterparts pp 71 75 Swing the Java user interface framework uses a specialised version of the classic MVC meant to support pluggable look and feel instead of applications in general p 75 GATE may be regarded as an MVC architecture in two ways do See ee ave e directly because we use the Swing toolkit for the GUIs e by analogy where LRs are models VRs are views and PRs are controllers Of these the latter sits least easily with the MVC scheme as PRs may indeed be controllers but may also not be A 1 3 Interfaces Architectural Principle The implementation of types should generally be hidden from the clients of the architecture Framework Expression With a few exceptions such as for utility classes clients of the framework work with the gate package This package is mostly composed of interface definitions Instantiations of these interfaces are obtained via the Factory class The subsidiary packages of GATE provide the implementations of the gate interfaces that are accessed via the factory They the
33. This chapter describes how to complete common tasks using GATE Sections that relate to the Development Environment are flagged D those that relate to the framework are flagged F sections relating to both are flagged D F There are two other primary sources for this type of information e for the development enviroment see the visual tutorials available on our movies page e for the framework see the example code at http gate ac uk GateExamples doc 2 1 Download GATE To download GATE point your web browser at http gate ac uk and follow the down load link Fill in the form there and you will be emailed an FTP address to download the system from Inttp gate ac uk demos movies html 14 A ey tO Se RO Se ee ee ee SS ee 2 2 Install and Run GATE GATE will run anywhere that supports recent versions of Java including Solaris Linux and Windoze platforms We don t run tests on other platforms but have had reports of successfull installs elsewhere e g MacOS X 2 2 1 The Easy Way The easy way to install is to use one of the platform specific installers created using Ze roG s InstallAnywhere product Download a platform specific installer and follow the instructions it gives you 2 2 2 The Hard Way Download one of the Java only release packages and follow the instructions below Prerequisites e A conforming Java 2 environment version 1 3 or above available free from Sun Mi crosystems
34. To appear 163 UV EV EV SEWN mene Bontcheva et al 02c K Bontcheva M Dimitrov D Maynard V Tablan and H Cunningham Shallow Methods for Named Entity Coreference Resolution In Chaines de r f rences et r solveurs d anaphores workshop TALN 2002 Nancy France 2002 To appear Booch 94 G Booch Object Oriented Analysis and Design 2nd Edn Benjamin Cummings 1994 Brugman et al 99 H Brugman K Bontcheva P Wittenburg and H Cunningham Integrating Mul timedia and Textual Software Architectures for Language Technology Technical report MPI TG 99 1 Max Planck Institute for Psycholinguistics Nijmegen Nether lands 1999 Campione et al 98 M Campione K Walrath A Huml and the Tutuorial Team The Java Tutorial Continued The Rest of the JDK Addison Wesley Reading MA 1998 Chinchor 92 N Chinchor Muc 4 evaluation metrics In Proceedings of the Fourth Message Un derstanding Conference pages 22 29 1992 Cobuild 99 C Cobuild editor English Grammar Harper Collins 1999 Cowie amp Lehnert 96 J Cowie and W Lehnert Information Extraction Communications of the ACM 39 1 80 91 1996 Cunningham 94 H Cunningham Support Software for Language Engineering Research Technical Report 94 05 Centre for Computational Linguistics UMIST Manchester 1994 Cunningham 99a H Cunningham A Definition and Short History of Language Engineering Journal of Natural Language Engineering 5
35. University of Sheffield 2000 Maynard et al 01 D Maynard V Tablan C Ursu H Cunningham and Y Wilks Named Entity FeV EVE Siw iVe Recognition from Diverse Text Types In Recent Advances in Natural Language Processing 2001 Conference Tzigov Chark Bulgaria 2001 Maynard et al 02a D Maynard H Cunningham K Bontcheva and M Dimitrov Adapting A Robust Multi Genre NE System for Automatic Content Extraction In The Tenth Inter national Conference on Artificial Intelligence Methodology Systems Applications AIMSA 2002 2002 Maynard et al 02b D Maynard V Tablan H Cunningham C Ursu H Saggion K Bontcheva and Y Wilks Architectural elements of language engineering robustness Journal of Natural Language Engineering Special Issue on Robust Methods in Analysis of Natural Language Data 2002 forthcoming Maynard et al 02c D Maynard K Bontcheva H Saggion H Cunningham and O Hamza Using a text engineering framework to build an extendable and portable IE based summarisation system In Proceedings of the ACL Workshop on Text Summarisation 2002 Maynard et al 02d D Maynard H Cunningham and R Gaizauskas Named entity recognition at sheffield university In H Holmboe editor Nordic Language Technology Arbog for Nordisk Sprogtechnologisk Forskningsprogram 2002 2004 pages 141 145 Museum Tusculanums Forlag 2002 McEnery et al 00 A McEnery P Baker R Gaizauskas and H Cun
36. annotations on documents JAPE grammars compile into cascaded finite state transducers Chapter 7 describes ANNIE a pipelined Information Extraction system which is supplied with GATE Chapter 8 describes CREOLE resources bundled with the system that don t fit into the previous categories Chapter 9 describes how to measure the performance of language analysis components Chapter 10 describes the data store security model Appendix A discusses the design of the system Appendix B describes the implementation details and formal definitions of the JAPE anno tation patterns language Appendix C describes in some detail the JAPE pattern grammars that are used in ANNIE for named entity recognition 1 5 Further Reading Lots of documentation lives on the GATE web server including e the main system documentation tree Unttp gate ac uk 2nttp gate ac uk gate doc aS SEE ey ic e tO Se SO Se ee eee ee SS ee e JavaDoc API documentation e HTML of the source code e parts of the requirements analysis that version 2 is based on For more details about Sheffield University s work in human language processing see the NLP group pages or Cunningham 99a For more details about Information Extraction see IE a User Guide or the Sheffield IE pages A list of publications on GATE and projects that use it some of which are available on line Cunningham et al 02 ACL 2002 describes the GATE fram
37. be broken down into various types of component known as resources Components are reusable software chunks with well defined interfaces and are a popular architectural form used in Sun s Java Beans and Microsoft s Net for example GATE components are specialised types of Java Bean and come in three flavours e LanguageResources LRs represent entities such as lexicons corpora or ontologies e ProcessingResources PRs represent entities that are primarily algorithmic such as parsers generators or ngram modellers e VisualResources VRs represent visualisation and editing components that participate in GUIs These definitions can be blurred in practice as necessary Collectively the set of resources integrated with GATE is known as CREOLE a Collection of REusable Objects for Language Engineering All the resources are packaged as Java Archive or JAR files plus some XML configuration data The JAR and XML files are made available to GATE by putting them on a web server or simply placing them in the local file space Section 1 3 2 introduces GATE s built in resource set When using GATE to develop language processing functionality for an application the developer uses the development environment and the framework to construct resources of the three types This may involve programming or the development of Language Resources 4The terms resource and component are synonymous in this context Resource
38. corpus This option saves all the annotations of a document together their features applying the restrictions previously discussed using the GateDocument dtd lt ELEMENT GateDocument GateDocumentFeatures TextWithNodes AnnotationSet gt lt ELEMENT GateDocumentFeatures Feature gt lt ELEMENT Feature Name Value gt lt ELEMENT Name PCDATA gt lt ELEMENT Value PCDATA gt lt ELEMENT TextWithNodes PCDATA Node gt lt ELEMENT AnnotationSet Annotation gt lt ATTLIST AnnotationSet Name CDATA IMPLIED gt lt ELEMENT Annotation Feature gt lt ATTLIST Annotation Type CDATA REQUIRED StartNode CDATA REQUIRED A ey OO e Se ee eee ee SS ee EndNode CDATA REQUIRED gt lt ELEMENT Node EMPTY gt lt ATTLIST Node id CDATA REQUIRED gt The document is saved under a name chosen by the user and it may have any extension However the recommended extension would be xml Using GATE s API this option is available by calling gate Document s toXml1 method This method returns a string which is the XML representation of the document on which the method was called Note It is recommended that the string representation to be saved on the file sys tem using the UTF 8 encoding as the first line of the string is lt xml version 1 0 encoding UTF 8 gt Example of such a GATE format document lt xml version 1 0 encoding UTF 8 7 gt lt GateDocument gt lt T
39. eR a CNP Ore CONLEX Jape x de Dad Gupta BEA Ao BE a SG HS WE RY TS loc context Jape e oa ea Pek e en a Pe Be ee Se Se es CAG Clean jape 25 Sc8 08 A AE le Be ek A BS References Chapter 1 Introduction Software documentation is like sex when it is good it is very very good and when it is bad it is better than nothing Anonymous There are two ways of constructing a software design one way is to make it so simple that there are obviously no deficiencies the other way is to make it so complicated that there are no obvious deficiencies C A R Hoare A computer language is not just a way of getting a computer to perform oper ations but rather that it is a novel formal medium for expressing ideas about methodology Thus programs must be written for people to read and only inci dentally for machines to execute The Structure and Interpretation of Computer Programs H Abelson G Sussman and J Sussman 1985 If you try to make something beautiful it is often ugly If you try to make something useful it is often beautiful Oscar Wilde GATE is an infrastructure for developing and deploying software components that process human language GATE helps scientists and developers in three ways 1 by specifiying an architecture or organisational structure for language processing software 2 by providing a framework or class library that implements the architecture and can be used to embed language processing capabi
40. grammar has 3 possible control styles brill first and appelt This is specified at the beginning of the grammar The brill style means that when more than one rule matches the same region of the document they are all fired The result of this is that a segment of text could be allocated more than one entity type and that no proiority ordering is necessary With the first style a rule fires for the first match that s found This makes it unappropiate for rules that end in or or Once a match is found the rule if fired it does not attempt to get a longer match as the other two styles do With the appelt style only one rule can be fired for the same region of text according to a set of priority rules Priority operates in the following way 1 From all the rules that match a region of the document starting at some point X the one which matches the longest region is fired 2 If more than one rule matches the same region the one with the highest priority is fired SS SE O OO UNES EE AE ee eee ee Se ee 3 If there is more than one rule with the same priority the one defined earlier in the grammar is fired An optional priority declaration is associated with each rule which should be a positive inte ger The higher the number the greater the priority By default if the priority declaration is missing all rules have the priority 1 i e the lowest priority For example the followin
41. i a MMA Rachie sonnei os mma VY LULE NAL RE SES ou they are called magic numbers For textual documents certain char sequences form such magic numbers Examples of magic numbers sequences will be provided in the Input section of each format supported by GATE All those tests are applied to each document read and after that a voting mechanism decides what is the best reader to associate with the document There is a degree of priority for all those tests The document s extension test has the highest priority If the system is in doubt which reader to choose then the one associated with document s extension will be selected The next higher priority is given to the web server s content type and the third one is given to the magic numbers detection However any two tests that identify the same mime type will have the highest priority in deciding the reader that will be used The web server test is not always successful as there might be documents that are loaded from a local file system and the magic number detection test is not always applicable In the next paragraphs we will se how those tests are performed and what is the general mechanism behind reader detection The method that detects the proper reader is a static one and it belongs to the gate DocumentFormat class It uses the information stored in the maps filled by the init method of each reader This method comes with three signatures static public DocumentFormat getD
42. is an end of a matched annotation 5 discard F 2 end for 3 if the set of active instances of FSM is empty then over true end while 2 if the set of accepting FSMs is not empty 1 from all accepting FSMs select the one that matched the longest path if there are more than one for the same path length select the one with highest priority 2 execute the action associated to the final state of the selected FSM instance 3 startNode selectedFSMInstance getLast Node get Next Node 3 else the matching failed gt start over from the next node startNode startN ode get Next Node 5 end while the set of active FSM instances can decrease when an active instance cannot continue there is no set of annotations starting from its current node that can be matched In this case it will be removed from the set if we do Brill style matching we have to process each of the accepting instances FE FRY eR Se Bee ae et ae ee S i VY B 4 Label Binding Scheme In TextPro a label binds to the last matched annotation in its scope A label binds to all the annotations matched in the scope In JAPE there is no label though there is a see below due to the ambiguity with Kleene In CPSL a constraint group can be both labelled and have a Kleene operator How can Kleene followed by label be distinguished from label E g given label are the constraints within the bracket
43. is used with a non priviliged session a SecurityException is thrown All security related classes and all their methods are documented in the Gate JavaDoc documentation java security package AccessController ac new AccessControllerImp1 ac open jdbc oracle thin GATEUSER gate machine ac uk 1521 GateDB Session mySession null try 1 mySession ac login myUser myPass ac findGroup myGroup getID catch gate security SecurityException ex ac close lt print some error and exit gt first check whether we have a valid session if ac isValidSession mySession ac close lt print some error and exit gt then check that it is an administrative session if mySession isPrivilegedSession ac close lt print some error and exit gt User myUser ac findUser myUser String myName myUser getName List myGroups myUser getGroups lt more code to access modify groups and users here gt we re done now just close the access controller connection ac close If you d like to use a dialog where the user can type those details the session can be obtained by using the login AccessController ac Component parent static method in the UserGroupEditor class The login code would then look as follows mySession UserGroupDialog login ac someParentWindow For a full example of code using the security API see TestSecurity java and SO SME A Seto pe fil i S
44. like infrastructural systems for Language Engineering Gaizauskas et al 96b Cunningham et al 97a Cunningham et al 96e ICTAI 1996 TITPSTER 1997 NeMLaP 1996 report work on GATE version 1 Cunningham et al 96c Cunningham et al 96d Cunningham et al 95 COLING 1996 AISB Workshop 1996 technical report report early work on GATE version 1 Cunningham et al 96b TIPSTER discusses a selection of projects in Sheffield using GATE version 1 and the TIPSTER architecture it implemented Cunningham et al 96a manual was the guide to developing CREOLE components for GATE version 1 Gaizauskas et al 96a manual was the user guide for GATE version 1 Humphreys et al 96 manual desribes the language processing components distributed with GATE version 1 Cunningham 94 Cunningham et al 94 NeMLaP 1994 technical report argue that software engineering issues such as reuse and framework construction are important for language processing R zD Never in the history of the Research Assessment Exercise has so much been owed by so many to so few exercises in copy and paste Chapter 2 How To The law of evolution is that the strongest survives Yes and the strongest in the existence of any social species are those who are most social In human terms most ethical There is no strength to be gained from hurting one another Only weakness The Dispossessed p 183 Ursula K le Guin 1974
45. of text in the document s content The documents supported by GATE have to be in one of the encodings accepted by Java The most popular is the UTF 8 encoding which is also the most storage efficient one for UNICODE If when loading a document in GATE the encoding parameter is set to the empty string then the default encoding of the platform will be used 5 5 1 Detecting the right reader When opening a document in GATE the file extension e g xml is important but if not present GATE uses some other means to detect its type In order to successfully apply the document creation algorithm described above GATE needs to detect the proper reader to use for each document format In order to do that it takes into consideration where possible the information provided by three sources e Document s extension e The web server s content type e Magic numbers detection The first represents the extension of a file like xml htm html txt sgm rtf etc the second represents the HTTP information sent by a web server regarding the content type of the document being send by it text html text xml etc and the third one represents certain sequences of chars which are ultimately number sequences GATE is capable to support multimedia documents if the right reader is added to the framework Sometimes multimedia documents are identified by a signature consisting in a sequence of numbers Inside GATE iw t MEM PATO Seto pe fil
46. of that feature for which the corresponding processing resource will be run When a conditional application is run over a document for each component that has an associated condition the value of the named feature is checked on the document and the component will only be used if the value entered by the user matches the one contained in the document features 2 13 D View Annotations To view a document double click on the filename in the left hand pane Note that it may take a few seconds for the text to be displayed if it is long The annotation types belonging to each annotation set are displayed to the right of the text If no application has been run the only annotations to be displayed will be those corresponding to the document format analysis performed automatically by Gate on loading the document e g HTML or XML tags If an application has been run other annotation types and or annotation sets may also be present The fonts and colours of the annotations can be edited by double clicking on the annotation name Select the annotation types to be viewed by clicking on the appropriate checkbox es The text segments corresponding to these annotations will be highlighted in the main text win dow Annotations relating to coreference if relevant are displayed separately If the Or thomatcher has been run a Coreference box will appear above the main window Clicking on this will bring up a pane containing coreference annotations belo
47. on UNIX you ve got this stuff already though you may need to install GNU make available at http www gnu org pat yourself on the back and sigh contentedly in the knowledge that your 1970s operating system is still the best available e An appreciation of natural beauty To build gate cd to gate build and 1 Start a shell if you re on Windoze you can use the cygnus bat script from the Cygwin distribution to do this Make sure GNU make is in your PATH 2 Run the script configure Ignore any insulting messages 3 Check that Makefile has the right paths for programs like java and javac If configure made a mistake edit the paths 4 Construct the dependency list and build the class files and a jar file containing the whole thing make depend make make jar 5 optional To test the system and build the Javadoc code documentation make test Note that DB tests may fail unless you can connect to Sheffield s Oracle server make docs Note that this needs an open network connection http www cygwin com Thttp www mirror ac uk sites sourceware cygnus com pub cygwin 8ftp www mirror ac uk SS SE O OO UNES EE AE ee ee Re SS ee 6 optional To build the developer Javadoc documentation including private members make internaldocs Note that you may need to change the name of the server for the Java platform documentation in the Makefile as we keep a copy on an internal server in Sheffield Those of you who u
48. one s creole xml file and the reader will be available to GATE We present for the rest of the section a complete three steps example of adding such a reader SS SE O OO Se RO Se ee eee ee SS IT The reader we describe in here is an XML reader Step 1 Create a new class called Xm1DocumentFormat that extends gate corpora TextualDocumentFormat Step 2 Implement the unpackMarkup Document doc which performs the required functionality for the reader Add XML detection means in init method public Resource init throws ResourceInstantiationExceptiont Register XML mime type MimeType mime new MimeType text xml Register the class handler for this mime type mimeString2ClassHandlerMap put mime getType mime getSubtype this Register the mime type with mine string mimeString2mimeTypeMap put mime getType mime getSubtype mime Register file sufixes for this mime type suffixes2mimeTypeMap put xml mime suffixes2mimeTypeMap put xhtm mime suffixes2mimeTypeMap put xhtml mime Register magic numbers for this mime type magic2mimeTypeMap put lt xm1 mime Set the mimeType for this language resource setMimeType mime return this initO More details about the information from those maps can be found in Section 5 5 1 Step 3 Add the following creole definition in the creole xml document lt RESOURCE gt lt NAME gt My XML Document F
49. server content type associate with plain text documents is text email The magic numbers test searches for keywords like Subject etc Output Same as plain text output 5 6 XML Input Output Support for input from and output to XML is described in section 5 5 2 In short e GATE will read any well formed XML document it does not attempt to validate XML documents Markup will by default be converted into native GATE format e GATE will write back into XML in one of two ways 1 Preserving the original format and adding selected markup for example to add the results of some language analysis process to the document 2 In GATE s own XML serialisation format which encodes all the data in a GATE Document as far as this is possible within a tree structured paradigm for 100 non lossy data storage use GATE s RDBMS or binary serialisation facilities see section 3 7 TAREA O OO UNES EE Se eee ee eRe SS ee When using the GATE framework object representations of XML documents such as DOM or jDOM or query and transformation languages such as X Path or XSLT may be used in parallel with GATE s own Document representation gate Document without conflicts Chapter 6 JAPE Regular Expressions Over Annotations If Osama bin Laden did not exist it would be necessary to invent him For the past four years his name has been invoked whenever a US president has sought to increase the defence budget or wriggle out of arm
50. themselves Object Oriented 3 5 The Lifecycle of a CREOLE Resource CREOLE resources exhibit a variety of forms depending on the perspective they are viewed from Their implementation is as a Java class plus an XML metadata file living at the same URL When using the development environment resources can be loaded and viewed via the resources tree left pane and the create resource mechanism When programming with the framework they are Java objects that are obtained by making calls to GATE s Factory class These various incarnations are the phases of a CREOLE resource s lifecycle Depending on what sort of task you are using GATE for you may use resources in any or all of these phases For example you may only be interested in getting a graphical view of what GATE s ANNIE Information Extraction system see chapter 7 does in this case you will use the GUI to load the ANNIE resources and load a document and create an ANNIE application and run it on the document If on the other hand you want to create your own resources or modify the Java code of an existing resource as opposed to just modifying its grammar for example you will need to deal with all the lifecylce phases The various phases may be summarised as Creating a new resource from scratch bootstrapping To create the binary image of a resource a Java class in a JAR file and the XML file that describes the resource to GATE you need to create the approp
51. these to recognise combinations of first names from the previous phase and surnames from their POS tags or case information Persons get marked with the annotation TempPerson We also percolate feature information about the gender from the previous annotations if known C 4 2 Location The rules for Location are fairly straightforward but we define them in this grammar so that any ambiguity can be resolved at the top level Locations are often combined with other entity types such as Organisations This is dealt with by annotating the two entity types separately and them combining them in a later phase Locations are recognised mainly by ci VIM ARC a Re Aeron a REE ae eS PR a Pe er i VY gazetter lookup using not only lists of known places but also key words such as mountain lake river city etc Locations are annotated as TempLocation in this phase C 4 3 Organization Organizations tend to be defined either by straight lookup from the gazetteer lists or for the majority by a combination of POS or case information and key words such as com pany bank Services Ltd etc Many organizations are also identified by contextual information in the later phase org_context jape In this phase organizations are annotated as TempOrganization C 4 4 Ambiguities Some ambiguities are resolved immediately in this grammar while others are left until later phases For example a Christian name followed by a po
52. these restrictions by type and branch and create compound restrictions of form Type t amp amp Attr Value amp amp Attra Valuez amp amp KK Atty Vatue or of form Type cl Ra rc ae en ite The grouping has to be done with care so it doesn t mix restrictions from different branches creating unnecessary restrictive queries These restrictions will be sent to the annotation graph which will do the matching for us Note that we can only reuse previous queries if the restrictions are identical on two branches 3 Create the data structures necessary for linking the bindings to the results of the queries see Fig B 5 TV A T A V 311 TV A T A V L2 Figure B 5 Building a compound matcher When this machine will be used for the actual matching the three queries will be run and the results will be stored in sets of annotations S1 53 in the picture and e For each pair of annotations from Aj Ag s t A in S amp Az in Sy 1 anew DFSM instance will be created 2 this instance will move to state 2 3 A1 Az will be bound to L 4 the corresponding node in the annotation graph will become max 4 endNode A2 endNode e Similarly for each pair of annotations from Aj A3 s t A in S amp Az in S3 1 a new DFSM instance will be created 2 this instance will move to state 3 3 A1 Az will be bound to Lo 4 the corresponding node in the annotation g
53. to import Than you can select files for the import of the data Prot g RDF format keeps the ontology data in two files classes file RDFS and instances file RDF 4 2 3 How to Save a Prot g project as RDF files If you do not choose the RDF file format on creation of new ontology or you do not open an RDF project you can use the popup menu item Save In Format and select RDF Schema format A ia o ic e e Se SO Se ee ee eRe SA ee 4 2 4 How to Set the Prot g plugin directory parameter in GATE The Prot g architecture allows integration of the plugin to extend the functionality of the application The RDF file format support is integrated in Prot g as a plugin included in the standard installation You can find all installed plugins in the subdirectory plugins of your Prot g installation Here you can add any other Prot g plugin you may want to have see http protege stanford edu plugins html If the Prot g plugins directory is accessible in GATE on creation of the Prot g project LR you should see a similar output in the GATE Messages tab Plugin classpath file D projects gate plugins file D projects gate plugins query_tab jar file D projects gate plugins rdf api jar file D projects gate plugins rdf_backend jar file D projects gate plugins standard_extensions jar file D projects gate plugins xerces jar Otherwise you will see only Plugin classpath without the list of as
54. versions of the pipelines see section 2 12 2 12 D Run PRs Conditionally on Document Features The Conditional Pipeline and Conditional Corpus Pipeline application types are condi tional versions of the pipelines mentioned in section 2 11 and allow processing resources to be run or not according to the value of a feature on the document In terms of graphical interface the only addition brought by the conditional versions of the applications is a box situated underneath the lists of available and selected resources which allows the user to choose whether the currently selected processing resource will run always never or only on the documents that have a particular value for a named feature IShttp gate ac uk gate src gate resources creole creole xml SS SE O OO UNES EE Se A eee Re SS ee If the Yes option is selected then the corresponding resource will be run on all the documents processed by the application as in the case of non conditional applications If the No option is selected then the corresponding resource will never be run the application will simply ignore its presence This option can be used to temporarily and quickly disable an application component for debugging purposes for example The If value of feature option permits running specific application components conditionally on document features When selected this option enables two text input fields that are used to enter the name of a feature and the value
55. warning will be issued and they will be discarded In the next subsections we will show how this options applies to the other formats supported by GATE 5 5 3 HTML Input The parser used to access HTML documents is the one provided by Java The documents are read and created in GATE the same way as the XML documents The extensions associate with the HTML reader are e htm e html The web server content type associate with html documents is text html The magic numbers test searches inside the document for the HTML lt htm1 signature There are certain HTML documents that do not contain the HTML tag so the magical numbers test might not hold There is a certain degree of customization for HTML documents in that GATE introduces new lines into the document s text content in order to obtain a readable form The annota tions will refer the pieces of text as described in the original document but there will be a few extra new line characters inserted After reading H1 H2 H3 H4 H5 H6 TR CENTER LI BR tags GATE will introduce a new line NL char into the text After a TITLE tag it will introduce two NLs With P tags GATE will introduce one NL at the beginning of the paragraph and one at the end of the paragraph All newly added NLs are not considered to be part of the text contained by the tag TAREA O ARE Se RO AE ee ee ee SS IT Vs Output The Save as XML option works exactly the same for all GATE s documents so there is no
56. 2 XML Input GATE permits the processing of any XML document and offers support for XML namespaces It benefits the power of Apache s Xerces parser and also makes use of Sun s JAXP layer Changing the XML parser in GATE can be achieved by simply replacing the value of a Java system property javax xml parsers SA XParserFactory GATE will accept any well formed XML document as input Although it has the possibility to validate XML documents against DTDs it does not do so because the validating procedure is time consuming and in many cases it issues messages that are annoying for the user There is an open problem with the general approach of reading XML HTML and SGML documents in GATE As we previously said the text covered by tags elements is appended to the GATE document content and a GATE annotation refers to this particular span of text When appending in cases such as end lt P gt lt P gt Start it might happen to concatenate the ending word of the previous annotation with the beginning phrase of the annotation currently being created resulting in a garbage input for GATE processing resources that operate at the text surface Let s take another example in order to better understand the problem lt title gt This is a title lt title gt lt p gt This is a paragraph lt p gt lt a href link gt Here is an useful link lt a gt When the markup is transformed to annotations it is likely that the text from the document
57. 50 5 Classes o alla as SRLS ete es els tee ae Bee 0 2 9 O59 a e A ee 150 BO Implementation A si Yak Keak Hee wee Bie 151 A ia e a e e a o Se A RR SA e B 01 A Walls Throweh store a A Aa a ee Bo Example RHS code 34 050 ge yg den Sy ae BG Compilation pierre ag be ea eB et a Se Be A ae C Named Entity State Machine Patterns Cel Mai Tape tesa Se a eh oe eck Boh ed oh ee ald wees ald ed ae Edo DESPERTE a Oe Fee ge BGS ge Ve a ES E frstname ape snk ong vod a heh Gk e e 24 wes 2S ee PA CA Mame ape wn oso see he eee Be ee A e tt cdas Al Person eu Se le Pree OA Re be ge ade Be dh eS a oe CA2 Locations i eo Us 4 A A a ees we ee Se oe C 4 3 Organization 3 5 sie dani a a dee aoe koe ay A Se e O A A Senay bh Sea Ae igh C 4 5 Contextual information 0 0 000202 eee C5 Mame Post jape k ok why ae ak eee My Shoe ee e Sho eu a ee eae A C0 ndate2pre Japen uniig Gare tae ace a ae a C7 date JADE tete dBi ba E Bia eae ae ee aie a bie aa a OS a a ale Ca reldatejaPe sneis dat Syke dy Py Bay a Ba C9 amber ape s paa eo oe eRe oe ee OP SO AA C TO address ape ets Sees dedi a Rikon eat ce a A aha Ae te es CIEL ape soe te ao acaba went oboe Yi at ae y we ee a la C12 Ten NGL ape a aria dae SS dds e G13 jobtitlejape 2 ssfsr e 2 afer eS 6 wee ee oe ere oP ee el ea ed AVG aes ea a ts Pe 2 a ee oy ee Pe ie Gt Ve oe hes Co UNKNOWN ape ane oh RA ee Re DERG e EERE E GS Mame context Ape e a os DP Be hee DA wah oe
58. A ee SS ES iia Rule Money1 e g 30 pounds AMOUNT_NUMBER SpaceToken kind space Lookup majorType currency_unit money gt money Number kind money rule Money1 7 8 1 Step 1 Tokenisation The tokeniser separates this phrase into the following tokens In general a word is comprised of any number of letters of either case including a hyphen but nothing else a number is composed of any sequence of digits punctuation is recognised individually each character is a separate token and any number of consecutive spaces and or control characters are recognised as a single spacetoken Token string Token string 800 kind number length 3 kind punctuation length 1 Token string 000 kind number length 3 SpaceToken string kind space length 1 Token string US kind word length 2 orth allCaps SpaceToken string kind space length 1 Token string dollars kind word length 7 orth lowercase 7 8 2 Step 2 List Lookup The gazetteer lists are then searched to find all occurrences of matching words in the text It finds the following match for the string US dollars Lookup minorType post_amount majorType currency_unit 7 8 3 Step 3 Grammar Rules The grammar rule for money is then invoked The macro MILLION_BILLION recognises any of the strings m
59. AME xml1FileUrl VALUE gate creole schema FacilitySchema xml gt lt AUTOINSTANCE gt lt AUTOINSTANCE gt lt PARAM NAME xml1FileUrl VALUE gate creole schema TokenSchema xml gt lt AUTOINSTANCE gt lt AUTOINSTANCE gt lt PARAM NAME xml1FileUrl VALUE gate creole schema SyntaxTreeNodeSchema xml gt lt AUTOINSTANCE gt lt AUTOINSTANCE gt lt PARAM NAME xml1FileUrl VALUE gate creole schema CorefSchema xml gt lt AUTOINSTANCE gt lt RESOURCE gt In order to create a gate creole AnnotationSchema object from a schema annotation file one must use the gate Factory class Eg FeatureMap params new FeatureMap param put xml1FileUrl annotSchemaFile toURL AnnotationSchema annotSchema Factory createResurce gate creole AnnotationSchema params SS SE O OO Se SO AE A ee SS ee Note All the elements and their values must be written in lower case as XML is defined as case sensitive and the parser used for XML Schema inside GATE searches is case sensitive In order to be able to write XML Schema definitions the ones defined in GATE resorces creole schema can be used as a model or the user can have a look at http www w98 org 2000 10 XMLSchema for a proper description of the semantics of the elements used Some examples of annotation schemas are given in Section 5 4 1 2 24 D Dump Results to File There are three main ways to dump out the results of for example some language analysis or I
60. Beans enabled builder http java sun com docs books tutorial javabeans whatis beanDef inition html SS SE O OO UNES EE Se eee eee ee SS ee tool can then examine the Bean s patterns discern its features and expose those features for visual manipulation A builder tool maintains Beans in a palette or toolbox You can select a Bean from the toolbox drop it into a form modify it s appearance and behavior define its interaction with other Beans and compose it and other Beans into an applet application or new Bean All this can be done without writing a line of code In GATE you develop sets of beans that do language processing tasks and then the framework wires them together without any code from you e Builder tools discover a Bean s features that is its properties methods and events by a process known as introspection Beans support introspection in two ways By adhering to specific rules known as design patterns when naming Bean features The Introspector class examines Beans for these design patterns to discover Bean features The Introspector class relies on the core reflection API The next section describes GATE s extended beans model 33 The GATE Framework We can think of the GATE framework as a backplane into which plug beans based CRE OLE components The user gives the system a list of URLs to search when it starts up and components at those locations are loaded by the system To be precise only
61. Developing Language Processing Components with GATE a User Guide For GATE version 2 1 beta 1 August 2002 Hamish Cunningham Diana Maynard Kalina Bontcheva Valentin Tablan Cristian Ursu Marin Dimitrov The University of Sheffield 2001 2002 http gate ac uk HTML version http gate ac uk sale tao This work has been supported by the Engineering and Physical Sciences Research Council EPSRC under grants GR K25267 and GR M31699 and by several smaller grants Id tao_main tex v 1 25 2002 07 18 15 48 19 hamish Exp http www dcs shef ac uk hamish http www dcs shef ac uk diana 3http www dcs shef ac uk kalina nttp www dcs shef ac uk valyt http www dcs shef ac uk cursu Shttp www sirma bg marin htm Brief Contents 1 Introduction 1 1 How to Use This Text ess ela See Be ce Bs ED A CONCERES 8 ets a ES ee She ay eh es Aa flag aA a d a i e 1 35 Overviews 4 6 5 a a a os 244 ws etd Se lS tt Gee 8 1 4 Structure of the Book 0 0 0 000000 00000000088 Fo Further Red a a een A ee eee ties 2 How To 2 1 Download GATE sorse ada Sok des Gey Ses Sa eee ew had ee eee S 22 Installand R n GATE stac a st a EAN 29 Troubleshooting ia ann e e Yeates A E e eat e ee ar oo 2 4 D Get Started with the GUI oaoa a ge we 25 DF Configure GATE acii LA A E A a e ah 2 6 Buld GATE s sa 6 eee a A e E Ed me ahaa od 2 7 D F Create a New CREOLE Resource oaoa a 2 8 F Instanti
62. E the GATE Component Model 3 1 The Weband CREOLE 10902 0 in tee e a a ea a amp 3 2 Java Beans a Simple Component Architecture 3 3 The GATE Framework de a ae a 3 4 Language Resources and Processing Resources o oo aaa 3 5 The Lifecycle of a CREOLE Resource a 3 6 Processing Resources and Applications ooo a a a 3 7 Language Resources and Datastores o o a 3 8 Built in CREOLE Resources a Visual CREOLE At Ontogazett lr 25 0 sosa Wok e AA 4 1 1 4 1 2 Gazetteer Lists Editor and Mapper Ontology HditOrs s 2 0 640A oft ha GS ary tea oP ee ae eee 4 2 Prot ee IM GA DE ati a ay dee Geen a a a 4 2 1 4 2 2 4 2 3 4 2 4 4 2 5 4 2 6 Opening Prot g projects and creating new ones How to Import RDF files in Prot g project How to Save a Prot g project as RDF files How to Set the Prot g plugin directory parameter in GATE How to save a Prot g ontology in Ontotext ontology file format Known problems and bugs o 37 37 38 38 39 40 40 41 42 42 42 43 44 44 45 47 48 49 49 49 50 ol 52 93 54 59 56 57 58 58 59 60 60 61 61 62 62 62 62 63 63 ALLAN OLEO NEU EU vs 5 Corpora Documents and Annotations 65 5 1 Features Simple Attribute Value Data 65 5 2 Corpora Sets of Documents plus Features
63. HS been evaluated and the effects of a rule application can only be referenced after the phase in which it occurs so the recognition power is no more than regular ERAS E Ie Cee are E ae Nn S itt B 3 Algorithms for JAPE Rule Application JAPE rules are applied in one of two ways Brill style where each rule is applied at every point in the document at which it matches Appelt style where only the longest matching rule is applied at any point where more than one might apply In the Appelt case the rule set for a phase may be considered as a single disjunctive expres sion and an efficient implementation would construct a single automaton to recognise the whole rule set To solve this problem we need to employ two algorithms e one that takes as input a CPSL representation and builds a machine capable of recog nizing the situations that match the rules and makes the bindings that occur each time a rule is applied This machine is a Finite State Machine FSM somewhat similar to a lexical analyser a deterministic finite state automaton e another one that uses the FSM built by the above algorithm and traverses the anno tation graph in order to find the situations that the FSM can recognise B 3 1 The first algorithm The first step that needs to be taken in order to create the FSM is to read the CPSL description from the external file s This is already done in the old version of Jape The second step is to build a nondeterminist
64. Matching special characters To specify a single or double quote as a string precede 1t with a backslash e g Token string will match a double quote For other special characters such as enclose it in double quotes e g Token category PRP Referring to previous annotations An annotation generated in one phase can be referred to in a later phase in exactly the same way as any other kind of annotation by specifying the name of the annotation within curly braces The features and values can be referred to or omitted as with all other annotations Make sure that if the Input specification is used in the grammar that the annotation to be referred to is included in the list Using context Specify left or right context around a pattern by enclosing it in round brackets outside the round brackets of the pattern In the example below the context in must precede the location to be annotated Only the location will be annotated but it is important to remember that context is consumed by the rule so it cannot be reused in another rule within the same phase So for example right context cannot be used as left context for another rule Rule InLoci in PARIS LN A bi e a e pe fil A id e ES OM ee nee VY LULE NAL RE SES Token string in Lookup majorType location Token orth allCaps LocName Debug Add the following to the options at the top of the grammar Options c
65. NK_TAG_1 gt BY lt CHUNK_TAG_2 gt IF CONDITION ON WORDS AND CONDITION ON POS TAGS AND CONDITION ON CHUNK TAGS In order to limit the number of possible rules to learn templates are defined that take into consideration windows around the chunk to be corrected the language bias We consider three words to the left three words to the right and the word itself three POS to the left three POS to the right and the POS itself and two chunks to the left and two chunks to the right So a possible rule is CHANGE I BY B IF POS_1 SEN This rule specifies to change the current I chunk by a B chunk when the previous POS tag is IN e g a preposition Rules are learnt until a user specified threshold is reached no improvement is ob served TAREA O EO E UNES EE AE A eee eRe Se ee iit In order to implement the chunker in GATE we have developed a pre chunking procedure a Java class that adds contextual information to each token We have also developed code for the automatic translation of the baseline system and the rules into JAPE grammars and we have defined a JAPE grammar that identifies chunks by means of regular expressions e g B D 8 4 OntoText Gazetteer The OntoText Gazetteer is a Natural Gazetteer implemented from the OntoText Lab http www ontotext com Its implementaion is based on simple lookup in several java util HashMap and is inspired by the strange idea of Atanas Kiryakov that searchin
66. O UNES EE Se ee ee ee SS ee vs has no parameters is automatically loaded when the creole xml data is loaded is not displayed to the GUI user it is used internally by the document creation code and is loaded from gate jar Resources may also have parameters of various types These resources from the GATE distribution illustrate the various types of parameters lt RESOURCE gt lt NAME gt GATE document lt NAME gt lt CLASS gt gate corpora DocumentImp1 lt CLASS gt lt INTERFACE gt gate Document lt INTERFACE gt lt COMMENT gt GATE transient document lt COMMENT gt lt OR gt lt PARAMETER NAME sourceUrl SUFFIXES txt text xml xhtm xhtml html htm sgml sgm mail email eml rtf COMMENT Source URL gt java net URL lt PARAMETER gt lt PARAMETER NAME stringContent COMMENT The content of the document gt java lang String lt PARAMETER gt lt 0R gt lt PARAMETER COMMENT Should the document read the original markup NAME markupAware DEFAULT true gt java lang Boolean lt PARAMETER gt lt PARAMETER NAME encoding OPTIONAL true COMMENT Encoding DEFAULT gt java lang String lt PARAMETER gt lt PARAMETER NAME sourceUrlStartO0ffset COMMENT Start offset for documents based on ranges OPTIONAL true gt java lang Long lt PARAMETER gt lt PARAMETER NAME sourceUrlEndO0ffset COMMENT End offset for documents based on ranges OPTIONAL true gt java lang Long lt PARAMETER gt lt PARAMETER NAME preserve0rig
67. Oracle datastores have advanced LR access rights based on users and groups which are similar to those in an operating system such as Linux In order to be able to access an LR stored in an Oracle datastore a user needs to supply a user name password and a group These credentials are used to determine which LRs are accessible to this user for reading and writing 10 2 1 Users Groups Sessions and Access Modes The security model provides primitives such as users groups permissions and sessions similar to the ones provided by the operating systems l Oracle installation is not provided with Gate You need to purchase this product separately from Oracle Corp see http www oracle com SS SE O EO TUNES EE AE A Re SS ee tat e users they are identified by login name and password each limited to 16 symbols A user may be member of one or more groups e groups identified by name up to 128 symbols e session each user must log into the datastore by providing name password and group in order to use its resources A session is opened when the user logs in The default inactivity period after which the session expires and the user should log into the datastore again is 4 hours e access modes there are four access modes in the present implementation The access Read Write to a resource according to its owner and access mode is shown in Table 10 1 World Read Group Read coop wee a Group
68. Processing Resource from the File menu adding them to the application and selecting the input and output Annotation Sets and any other parameters as necessary Flexible Exporter At load time the following parameters can be set for the flexible exporter e includeFeatures if set to true features are included with the annotations exported if false the default status they are not e useSuffixForDumpFiles if set to true the default status the output files have the suffix defined in suffixForDumpFiles if false no suffix is defined and the output file simply overwrites the existing file but see the outputFileUrl runtime parameter for an alternative TAREA O ARIEL E UNES ES AE ee ee ee SS ee e suffixForDumpFiles this defines the suffix if useSuffixForDumpFiles is set to true By default the suffix is gate The following runtime parameters can also be set after the file has been selected for the application e annotationSetName this enables the user to specify the name of the annotation set which contains the annotations to be exported If no annotation set is defined it will use the Default annotation set e annotationTypes this contains a list of the annotations to be exported By default it is set to Person Location and Date e dumpTypes this contains a list of names for the exported annotations If the annota tion name is to remain the same this list should be identical to the list in annotation T
69. R EE a eS a a er 1Uv C 10 address jape Rules for Address cover ip addresses phone and fax numbers and postal addresses In general these are not highly ambiguous and can be covered with simple pattern matching although phone numbers can require use of contextual information Currenly only UK formats are really handled though handling of foreign zipcodes and phone number formats is envisaged in future The annotations produced are of type Email Phone etc and are then replaced in a later phase with final Address annotations with phone etc as features C 11 url jape Rules for email addresses and Urls are in a separate grammar from the other address types for the simple reason that Space Tokens need to be identified for these rles to operate whereas this is not necessary for the other Address types For speed of processing we place them in separate grammars so that Space Tokens can be eliminated from the Input when they are not required C 12 i dentifier jape This grammar identifies Identifiers which basically means any combination of numbers and letters acting as an ID reference number etc not recognised as any other entity type C 13 jobtitle jape This grammar simply identifies Jobtitles from the gazetteer lists and adds a JobTitle anno tation which is used in later phases to aid recognition of other entity types such as Person and Organization It may then be discarded in the Clean phase if not required as a fi
70. Read omawia Owner Read omame 7 Y o l Table 10 1 Access Modes When GATE is configured for use with Oracle a superuser and group are created e super user ADMIN password sesame e administrative group ADMINS The superuser is similar to the root user in Unix and has access to any resource despite its access mode This user can also create or remove other users We recommend that you change the password of the superuser immediately after you have installed the Oracle support for GATE 10 2 2 User Group Administration Running the administration tool When GATE Oracle tables are first created with the database install scripts they only contain the ADMIN user which is the only user who can create and modify users and groups We do not recommend using the ADMIN user to store access LRs in GATE 3This user is similar to the root user in Unix operating systems SS oi a eee ae ae EE Se ee EE ee LL Instead immediately after installing Oracle support for Gate datastores some users and groups must be created by running the UserGroupEditor tool Before running this tool the URL to the Oracle database needs to be specified in gate xml either the user s own or the site wide gate xml An example entry is DBCONFIG url jdbc oracle thin GATEUSER gate example dcs shef ac uk 1521 gate101 urll jdbc oracle thin GATEUSER gate testdb dcs shef ac uk 1521 gate02 The example entry shows that there
71. S 3 The pattern description is followed by a label for the annotation A label is denoted by a preceding semi colon in the example below the label is location The RHS of the rule contains information about the annotation Information about the an notation is transferred from the LHS of the rule using the label just described and annotated with the entity type which follows it Finally attributes and their corresponding values are added to the annotation Alternatively the RHS of the rule can contain Java code to create or manipulate annotations In the simple example below the pattern described will be awarded an annotation of type Enamex because it is an entity name This annotation will have the attribute kind with value location and the attribute rule with value GazLocation The purpose of the rule attribute is simply to ease the process of manual rule validation Rule GazLocation Lookup majorType location location gt location Enamex kind location rule GazLocation It is also possible to have more than one pattern and correspsonding action as shown in the rule below On the LHS each pattern is enclosed in a set of round brackets and has a unique label on the RHS each lable is associated with an action In this example the Lookup annotation is labelled jobtitle and is given the new annotation JobTitle the TempPerson annotation is labelled pers
72. S AE A ee SS IT There are three ways of adding documents to a corpus 1 When creating the corpus clicking on the icon under Value brings up a popup window with a list of the documents already loaded into Gate This enables the user to add any documents to the corpus 2 Alternatively the corpus can be loaded first and documents added later by double clicking on the corpus and using the and icons to add or remove documents to the corpus Note that the documents must have been loaded into Gate before they can be added to the corpus 3 Once loaded the corpus can be populated by right clicking on the corpus and selecting Populate With this method documents do not have to have been previously loaded into Gate as they will be loaded during the population process Select the directory containing the relevant files choose the encoding and check or uncheck the recurse directories box as appropriate The initial value for the encoding is the platform default To add a new annotation schema simply choose the name and the path or Url For more information about schema see 5 4 1 2 9 2 Loading Processing Resources This section describes how to load and run CREOLE resources not present in ANNIE To load ANNIE see Section 2 14 For technical descriptions of these resources see Chapter 8 All these resources are loaded by selecting them from the set of Processing Resources right click on Processing Resources or select New
73. The Orthomatcher module adds identity relations between named entities found by the semantic tagger in order to perform coreference It does not find new named entities as such but it may assign a type to an unclassified proper name using the type of a matching name The matching rules are only invoked if the names being compared are both of the same type i e both already tagged as say organisations or if one of them is classified as unknown iw f MEM PATO Seto pe fil i a MMA a os A VY ELULE NAL RE SES UD This prevents a previously classified name from being recategorised 7 6 1 GATE Interface Input entity annotations with an id attribute Output matches attributes added to the existing entity annotations 7 6 2 Resources A lookup table is used to record non matching strings which represent the same entity e g IBM and Big Blue Coca Cola and Coke The table is only read in at compile time and since the module is tight coupled the whole GATE executable must be recompiled to include any changes 7 6 3 Processing The wrapper builds an array of the strings types and IDs of all name annotations which is then passed to a string comparison function for pairwise comparisons of all entries 7 7 Pronominal Coreference The pronominal coreference module performs anaphora resolution using the JAPE grammar formalism Note that this module is not automatically loaded with the other ANNIE mod
74. am et al 00d and Cunningham 99c technical reports document early versions of JAPE superceded by the present document Cunningham et al 00a Cunningham et al 98a and Peters et al 98 OntoLex 2000 LREC 1998 presents GATE s model of Language Resources their access and distri bution Maynard et al 00 technical report surveys users of GATE up to mid 2000 Cunningham et al 00c and Cunningham et al 99 COLING 2000 AISB 1999 summarise experiences with GATE version 1 Cunningham et al 00b LREC 2000 taxonomises Language Engineering components and discusses the requirements analysis for GATE version 2 Bontcheva et al 00 and Brugman et al 99 COLING 2000 technical report de scribe a prototype of GATE version 2 that integrated with the EUDICO multimedia markup tool from the Max Planck Institute 21http www emille lancs ac uk http www mpi nl world tg lapp eudico eudico html TAE EE O TIENE Se E AAA ee ee ee SS ee Gamb ck amp Olsson 00 LREC 2000 discusses experiences in the Svensk project which used GATE version 1 to develop a reusable toolbox of Swedish language processing components Cunningham 99a JNLE reviewed and synthesised definitions of Language Engineering Stevenson et al 98 and Cunningham et al 98b ECAI 1998 NeMLaP 1998 re port work on implementing a word sense tagger in GATE version 1 Cunningham et al 97b ANLP 1997 presents motivation for GATE and GATE
75. any other document 2 17 D Save Resource Parameter State to File Resources and applications that are made up of them are created based on the settings of their parameters see section 2 9 It is possible to save the data used to create a an application to file and re load it later To save the application to file right click on it in the resources tree and select Save application state which will give you a file creation dialogue To restore the application later select Restore application from file from the File menu Note that the data that is saved represents how to recreate an application not the resources that make up the application itself So for example if your application has a resource that initialises itself from some file e g a grammar then that file must still exist when you restore the application The file resulted from saving the application state contains the values of the initialisation parameters for all the processing resources contained by the stored application For the pa rameters of type URL which are typically used to select external resources such as grammars or rules files a transformation is applied so that all the paths are relative to the location of the file used to store the state This means that the resource files used by an application do not need to be in the same location as when the application was initially created but rather in the same location relative to the location o
76. are two databases configured for this site one at each URL There is no limit to the number of Oracle databases one can have but they all need to have an attribute starting with url e g urll url2 To run the tool call the gate script with the a parameter When the tool starts up it first asks you to select which Oracle database you wish to administer All databases defined in the DBCONFIG section of gate xml will be shown in a listbox Once the database is chosen a login dialog is shown asking for the user name password and group of the ADMIN user The initial password of the ADMIN user is sesame and the group is ADMINS We advise that these are changed the first time this tool is run If all login credentials are provided correctly the graphical tool starts up EGATI User Group Admin valentin kalina hamish diana cristian ADMIN m 3 Figure 10 1 The User Group Administration Tool a A eee ee ae a EE ee ee al a tad Viewing user and group information As shown in Figure 10 1 the user group administration tool called the UG tool for the rest of this section consist of two parallel lists By default the left one shows a list of all users in the database and the right one is empty To view the groups to which a particular user belongs you need to select that user in the list Then the right list displays this user s groups If the list remains empty then it means that this
77. arious renaming options such as renaming the annotations and the file For information on how to use the flexible exporter see Section 2 9 2 8 6 Annotation Set Transfer The Annotation Set Transfer enables the parts of a document matching a particular anno tation to be transferred into a new annotation set For example this can be used when a user only wants to run a processing resource over a specific part of a document such as the Body of an HTML document The user specifies the name of the annotation set and the annotation which covers the part of the document they wish to transfer and the name of the new annotation set All the other annotations corresponding to the matched text will be transferred to the new annotation set For example we might wish to perform named entity recognition on the body of an HTML text but not on the headers After tokenising and performing gazetteer lookup on the whole text we would use the Annotation Set Transfer to transfer those annotations created by the tokeniser and gazetteer into a new annotation set and then run the remaining NE resources such as the semantic tagger and coreference modules on them For more information about how to use the Annotation Set transfer see Section 2 9 2 Chapter 9 Performance Evaluation of Language Analysers When you can measure what you are speaking about and express it in numbers you know something about it but when you cannot measure it when you cannot
78. ase on Recall increase on SPURIOUS Organization Ihuman marked from 0 75 to human marked from 0 375 to ANNOTATIONS in the Lo 0 75 automatic texts PARTIALL Y CORRECT ho ANNOTATIONS in the automatic texts D 9444444444444444 Annotation type Precision increase on Person human marked from 0 893947368421 052632 to 0 9444444444444444 0 9444444444444444 1 0 en type Recall increase on human marked from 0 8571426571426571 to 1 0 Figure 9 2 Fragment of results from benchmark tool 9 4 Metrics for Evaluation in Information Extraction Much of the research in IE in the last decade has been connected with the MUC com petitions and so it is unsurprising that the MUC evaluation metrics of precision recall and F measure Chinchor 92 also tend to be used along with slight variations These metrics have a very long standing tradition in the field of IR van Rijsbergen 79 see also Manning amp Sch tze 99 Frakes amp Baeza Yates 92 Precision measures the number of correctly identified items as a percentage of the number of items identified In other words it measures how many of the items that the system identified were actually correct regardless of whether it also failed to retrieve correct items The higher the precision the better the system is at ensuring that what is identified is correct Error rate is the inverse of precision and measures the number of incorrectly identified items as a percenta
79. ate CREOLE Resources ooa 0 2 9 D Load CREOLE Resources ooa 0000 eee ee ee ee 2 10 D F Configure CREOLE Resources o o e 2 11 D Create and Run an Application o o o 2 12 D Run PRs Conditionally on Document Features 2 13 D View Annotations 2 2 4 oa dara eo ee a ed et 2 14 D Do Information Extraction with ANNIE 2 15 D Create and Edit Test Data sac 0 5 toa Single aR et A 2 16 D Save and Restore LRs in Data Stores 2 2 ee 2 17 D Save Resource Parameter State to File 2 2 aaa 2 18 D F Perform Evaluation with the AnnotationDiff tool 2 19 D Use the Corpus Benchmark Evaluation tool 2 20 D Write JAPE Grammars e ta so ea Be ete E 2 21 F Embed NLE in other Applications o o o 2 22 D F Add support for a new document format 2 23 D F Create a New Annotation Schema o o o 2 24 D Dump Results to File 24 00 2s mae dee ee ee ae ee a 2 25 D Step GUI Freezing on Linux 20d ao ha 2 aed 22 x 2 26 D Stop GUI Crashing on Linux 2 ore ee AAA 2 27 D Stop GATE Restoring GUI Sessions Options 228 Worl with Unicode 4c uit et Gu ite eS AA Ee aS a eR et 229 Work Wal TOCA Eye A A 3 CREOLE the GATE Component Model 3 1 The Web and CREOLE 224020 oh wa a ade id oe 3 2 Java Beans a Simple Component Architecture 3
80. atment of spaces and control characters In this way only the first jape file needs to be changed for each grammar set rather than every individual grammar The first jape grammar also has a dummy rule in This is never intended to fire it is simply added because every grammar set must contain rules but there are no specific rules we wish to add here Even if the rule were to match the pattern defined it is designed not to produce any output due to the empty RHS C 3 firstname jape This grammar contains rules to identify first names and titles via the gazetteer lists It adds a gender feature where appropriate from the gazeteer list This gender feature is used later in order to improve co reference between names and pronouns The grammar creates separate annotations of type FirstPerson and Title C 4 name jape This grammar contains initial rules for organization location and person entities These rules all create temporary annotations some of which will be discarded later but the majority of which will be converted into final annotations in later grammars Rules beginning with Not are negative rules this means that we detect something and give it a special annotation or no annotation at all in order to prevent it being recognised as a name This is because we have no negative operator we have but not C 4 1 Person We first define macros for initials first names surnames and endings We then use
81. ay of the week 6 1 Use of Context Context can be dealt with in the grammar rules in the following way The pattern to be annotated is always enclosed by a set of round brackets If preceding context is to be included in the rule this is placed before this set of brackets This context is described in exactly the same way as the pattern to be matched If context following the pattern needs to be included it is placed after the label given to the annotation Context is used where a pattern should only be recognised if it occurs in a certain situation but the context itself does not form part of the pattern to be annotated For example the following rule for Time assuming an appropriate macro for year would mean that a year would only be recognised if it occurs preceded by the words in or by TAREA O TAE TENERSE SE vee eee AE SS IS wi Rule YearContexti Token string in Token string by YEAR date gt date Timex fkind date rule YearContext1 Similarly the following rule assuming an appropriate macro for email would mean that an email address would only be recognised if it occurred inside angled brackets which would not themselves form part of the entity Rule Emailaddress1 Token string lt EMAIL email Token string gt gt email Address kind email rule Emailaddress1 6 2 Use of Priority Each
82. bots direct face to face contact is an extremely important part of human life It helps develop self understanding and the growth of a healthy personality You just have a different relationship to somebody when you re looking at them than you do when you re punching away at a keyboard and some symbols come back I suspect that extending that form of abstract and remote relationship instead of direct personal contact is going to have unpleasant effects on what people are like It will diminish their humanity I think Chomsky quoted at http photo net wtr dead trees 53015 htm The GATE architecture is based on components reusable chunks of software with well defined interfaces that may be deployed in a variety of contexts The design of GATE is based on an analysis of previous work on infrastructure for LE and of the typical types of software entities found in the fields of NLP and CL see in particular chapters 4 6 of Cunningham 00 Our research suggested that a profitable way to support LE software development was an architecture that breaks down such programs into components of various types Because LE practice varies very widely it is after all predominantly a research field the architecture must avoid restricting the sorts of components that developers can plug into the infrastructure The GATE framework accomplishes this via an adapted version of the Java Beans component framework from Sun Section 3 2 describes Java
83. ce LR all LRs have a FeatureMap a Java Map asso ciated with them that stored attribute value information about the resource FeatureMaps are also used to associate arbitrary information with ranges of documents e g pieces of text via the annotation model see below Documents have a DocumentContent which is a text at present future versions may add support for audiovisual content and one or more AnnotationSets which are Java Sets 5 3 Documents Content plus Annotations plus Fea tures Documents are modelled as content plus annotations see section 5 4 plus features see section 5 1 The content of a document can be any subclass of DocumentContent 5 4 Annotations Directed Acyclic Graphs Annotations are organised in graphs which are modelled as Java sets of Annotation An notations may be considered as the arcs in the graph they have a start Node and an end Node an ID a type and a FeatureMap Nodes have pointers into the sources document e g character offsets 5 4 1 Annotation Schemas Annotation schemas provide a means to define types of annotations in GATE GATE uses the XML Schema language supported by W3C for these definitions When us ing the development environment to create edit annotations a component is available gate gui SchemaAnnotationEditor which is driven by an annotation schema file This component will constrain the data entry process to ensure that only annotations that corre spond to a particular
84. code similar to that used in compiler compilers Confused If not welcome to the domain of the nerds which is where you belong I m sure you ll be happy here Appendix C Named Entity State Machine Patterns There are it seems to me two basic reasons why minds aren t computers The first is that human beings are organisms Because of this we have all sorts of needs for food shelter clothing sex etc and capacities for locomotion ma nipulation articulate speech etc and so on to which there are no real analogies in computers These needs and capacities underlie and interact with our men tal activities This is important not simply because we can t understand how humans behave except in the light of these needs and capacities but because any historical explanation of how human mental life developed can only do so by looking at how this process interacted with the evolution of these needs and capacities in successive species of hominids The second reason is that brains don t work like computers Minds Machines and Evolution Alex Callinicos 1997 ISJ 74 p 103 This chapter describes the individual grammars used in GATE for Named Entity Recog nition and how they are combined together It relates to the default NE grammar for ANNIE but should also provide guidelines for those adapting or creating new grammars For documentation about specific grammars other than this core set use this document in comb
85. condition that annotation is discarded and a message is issued by GATE This option makes possible to generate an XML document with tags surrounding the an notation s refereed text and feature saved as attributes All features which are collections strings or numbers are saved and the others are discarded However when read back only the attributes under the GATE namespace see bellow are reconstructed back different than iw t MEM PATO Seto pe fil i a MMA ES OM ee nee VY LULE NAL RE SES ev the others That is because GATE does not store in the XML document the information about the features class and for collections the class of the items So when read back all features will become strings except those under the GATE namespace One will notice that all generated tags have an attribute called gateld under the names pace http www gate ac uk The attribute is used when the document is read back in GATE in order to restore the annotation s old ID This feature is needed because it works in close cooperation with another attribute under the same namespace called matches This attribute indicates annotations tags that refer the same entity They are under this namespace because GATE is sensitive to them and treats them differently then all other elements with their attributes which falls under the general reading algorithm described at the beginning of this section The gateld under GATE namespace is used to
86. create an annotation which have as ID the value indicated by this attribute The matches attribute is used to create an ArrayList in which the items will be Integers representing the ID of annotations that the current one matches Example If the text being processed is as follows lt Person gate gateId 23 gt John lt Person gt and lt Person gate gateId 25 gate matches 23 25 30 gt John Major lt Person gt are the same person What GATE does when parses this text is to create two annotations al type Person al ID Integer 23 al start lt the start offset of John gt al end lt the end offset of John gt al featureMap 1 a2 type Person a2 ID Integer 25 a2 start lt the start offset of John Major gt a2 end lt the end offset of John Major gt a2 featureMap matches Integer 23 Integer 25 Integer 30 It s not an XML entity but a information extraction named entity SS SE O OO Se SO Se eee ee ee SA ee Under GATE s API this option is available by calling gate Document s toXml Set aSetContainingAnnotations method This method returns a string which is the XML representation of the document on which the method was called If called with null as a parameter then the method will attempt to restore only the original markup If the param eter is a set that contains annotations then each annotation is tested against the crossover restriction and for those found to violate it a
87. deal put WayForward on a very sound financial foundation Its moral foundation on the other hand is not something I would 125 TAREA O EO UNES EE AE A ee Se ee 1 a want to trust my weight to I ve recently been analysing a lot of the arguments put forward in favour of the Star Wars project and if you know what you re looking for the pattern of the algorithms is very clear So much so in fact that looking at Pentagon policies over the last couple of years I think I can be fairly sure that the US Navy is using version 2 00 of the program while the Air Force for some reason only has the beta test version of 1 5 Odd that Dirk Gently s Holistic Detective Agency Douglas Adams 1987 pp 55 56 This chapter describes the LR access mechanism which is implemented for persistent LRs At present there are two LR persistency storage methods Java serialisation and Oracle Here we will describe their security features in turn 10 1 Java serialisation and LR access rights At present the security model is not implemented for Java serialization One should rely on the security control offered by the OS in order to restrict access to certain persistent resources 10 2 Oracle Datastore and LR access rights Warning These features will not work unless you have an Oracle pre installed at your site and you or an administrator at your site has installed the GATE Oracle support see http gate ac uk gate doc persistence pdf
88. e e Databases like Oracle e Java serialization e XML serialization We describe the latter case in here XML persistence doesn t necessarily preserve all the objects belonging to the annotations documents or corpora Their features can be of all kinds of objects with various layers of nesting For example lists containing lists containing maps etc Serializing these arbitrary data types in XML is not a simple task GATE does the best it can and supports native Java types such as Integers and Booleans but where complex data types are used information may be lost the types will be converted into Strings GATE provides a full serialization of certain types of features such as collections strings and numbers It is possible to serialize only those collections containing strings or numbers The rest of other features are serialized using their string representation and when read back they will be all strings instead of being the original objects Consequences of this might be observed when performing evaluations see the evaluation section When GATE outputs an XML document it may do so in one of two ways iw t MEM PATO Seto pe fil i fe MEA a os A VY LULE NAL RE SES ey e When the original document that was imported into GATE was an XML document GATE can dump that document back into XML possibly with additional markup added e For all document formats GATE can dump its internal representation of the document into XML
89. e Section 2 23 or the Unrestricted Annotation Editor can be used see Section 2 15 2 To add an annotation select the annotation type from the dropdown list If the annota tion type can have features associated with it according to its definition in the annotation schema a list of possible features will appear Use the arrows to select those features re quired and fill in the values as appropriate Click OK to create the annotation and return to the main Gate window The new annotation should now appear in the annotations list The Annotation Schema Editor can also be used to edit an existing annotation e g to change the values of the features associated with it This is done by right clicking on the annotation in the text selecting the annotation set annotation name and Select and then right clicking on the annotation in the lower pane and selecting Edit This will bring up the relevant Annotation Schema Editor for that annotation 2 15 2 Unrestricted Annotation Editor If an annotation schema does not exist or if the user wishes to have more flexibility in defin ing the annotation e g to add new features not present in the schema the Unrestricted Annotation Editor should be used From the Edit Annotations window select Unre stricted annoation editor and enter the annotation name and feature names and values It is important to remember to click on Add feature before clicking on OK and returnin
90. e MME RS A PRA VY ELULE NAL RE SES UserGroupEditor java ive Appendices 133 Appendix A Design Notes Why has the pleasure of slowness disappeared Ah where have they gone the amblers of yesteryear Where have they gone those loafing heroes of folk song those vagabonds who roam from one mill to another and bed down under the stars Have they vanished along with footpaths with grasslands and clearings with nature There is a Czech proverb that describes their easy indolence by a metaphor they are gazing at God s windows A person gazing at God s windows is not bored he is happy In our world indolence has turned into having nothing to do which is a completely different thing a person with nothing to do is frustrated bored is constantly searching for an activity he lacks Slowness Milan Kundera 1995 pp 4 5 GATE is a backplane into which specialised Java Beans plug These beans are loose coupled with respect to each other they communicate entirely by means of the GATE framework Inter component communication is handled by model components LanguageResources and events Components are defined by conformance to various interfaces e g LanguageResource ensuring separation of interface and implementation The reason for adding to the normal bean initialisation mech is that LRs PRs and VRs all have characteristic parameterisation phases the GATE resources components model makes explicit these phases
91. e annotation set from the key document that also holds annotations of the type specified in the annotationSchema parameter If it s set to null then the default annotation set will be used TAREA O OO E UNES EE Se eee ee ee SS ee responseAnnotationSetName same as for the key annotation set keyFeatureNamesSet a Set containing keys the keys of the features from the annotation key set taken into consideration when performing the diff If it is null then all the features from the key set will be taken into consideration If it is an empty set then no feature will be taken into consideration Otherwise only the keys specified in this set will be taken into consideration by the evaluation process text Mode if set to true it will not construct the graphic components like for example the table displaying the results The structure used for building the graphic representation will be constructed and AnnotationDiff will provide means to access its results through its get methods By default this parameter is set to false 2 18 4 Reading the results from the Annotation Diff By using the API all the calculated measures cam be accessed through getZZZ methods Example annotDiff getPrecisionAverage annotDiff getPrecisionLenient annotDiff getPrecisionStrict All four types of classified annotations can be accessed using only one special get method called getAnnotationsOf Type Example AnnotationSet correctAnnot
92. e application with a single document loaded There Gate 2 0rc1 build 817 Mi x File Options Tools Help ES cate Messages GATE document_0001F G amp Applications Text Annotations Annotation Sets Print 3 i All Language Resources The Department of Computer Science University of Sheffield Default annotations 2 GATE document_0001 F originai martups a 2 Processing Resources Catest News Last updated 17th December 2001 DCS is v j Data stores awarded RAE2001 Grade 5 Ol Prospective Students Current Students Staff Visitors Dept Contact Details Site Map Search University CO O a The Department of Computer Science Regent Court 4 gt j 211 Portobello Street Sheffield g a JO r Original markups 63 74 l hret deptiatesthtm Original markups 543 552 href mailto webmaster Original markups 144 144 href staft htm isEmpty Annotations Editor Features Editor Finished dumping into the file Ztmplthing2 zm Figure 2 1 Main Window are five main areas of the window 1 the menus bar along the top with File etc 2 in the top left of the main area a tree starting from Gate and containing Applica tions Language Resources etc this is the resources tree 1http gate ac uk demos movies html SS SE O OO E TUNES EE AE ee ee eR SS ee 3 in the bottom left of the main a
93. e coreference chain contains the IDs of the annotations entities that co refer e A new Coreference annotation is created for each chain The annotation contains a single feature matches which value is the coreference chain the list with IDs The annotations are exported in a pre specified annotation set The resolution for she her her he him his herself and himself is similar because the analysis of the corpus showed that these pronouns are related to their antecedents in similar manner The characteristics of the resolution process are e Context inspected is not very big cases where the antecedent is found more than 3 sentences further back than the anaphor are rare e Recency factor is heavily used the candidate antecedents that appear closer to the anaphor in the text are scored better e Anaphora have higher priority than cataphora If there is an anaphoric candidate and a cataphoric one then the anaphoric one is preferred even if the recency factor scores the cataphoric candidate better The resolution process performs the following steps e Inspect the context of the anaphor for candidate antecedents A candidate is considered every Person annotation Cases where she her refers to inanimate entity ship for example are not handled e For each candidate perform a gender compatibility check only candidates having gender feature equal to unknown or compatible with the pronoun are considered for furt
94. e implementation and extension of the original is available from http gate ac uk download This book describes how to use GATE to develop language processing components test their performance and deploy them as parts of other applications In the rest of this chapter e section 1 1 describes the best way to use this book e section 1 2 briefly notes that the context of GATE is applied language processing or Language Engineering e section 1 3 gives an overview of developing using GATE e section 1 4 describes the structure of the rest of the book e section 1 5 lists other publications about GATE Note if you don t see the component you need in this document or if we mention a compo nent that you can t see in the software contact gate dcs shef ac uk various components are developed by our collaborators who we will be happy to put you in contact with Often the process of getting a new component is as simple as typing the URL into GATE the system will do the rest 1 1 Howto Use This Text It is a good idea to read all of this introduction you can skip sections 1 2 and 1 5 if pressed then you can either continue wading through the whole thing or just use chapter 2 as a 2This is a restricted form of the GNU licence which means that GATE can be embedded in commercial products if required TAREA O OO E UNES EE Se eee ee ee SS ee reference and dip into other chapters for more detail as necessary Chapter 2 gives
95. e is in Doug Appelt s TextPro manual Doug was a great help to us in implementing JAPE Thanks Doug 86 iw f MEM PATO Seto pe fil i a MMA a os A VY LULE NAL RE SES UY and GATE s model of annotations is based on graphs Hmmm Another way of saying this typically regular expressions are applied to character strings a simple linear sequence of items but here we are applying them to a much more complex data structure The result is that in certain cases the matching process in non deterministic i e the results are dependent on random factors like the addresses at which data is stored in the virtual machine when there is structure in the graph being matched that requires more than the power of a regular automaton to recognise JAPE chooses an alternative arbitrarily However this is not the bad news that it seems to be as it turns out that in many useful cases the data stored in annotation graphs in GATE and other language processing systems can be regarded as simple sequences and matched deterministically with regular expressions A JAPE grammar consists of a set of phases each of which consists of a set of pattern action rules The phases run sequentially and constitute a cascade of finite state transducers over annotations The left hand side LHS of the rules consist of an annotation pattern that may contain regular expression operators e g The right hand side RHS consists of annotation manipulation statements Anno
96. eResource gate corpora DocumentImpl params The document created here is transient when you quit the JVM the document will no longer exist If you want the document to be persistent you need to store it in a DataStore Assuming that you have a DataStore already open called myDataStore this code will ask the data store to take over persistence of your document and to synchronise the memory representation of the document with the disk storage Document persistentDoc myDataStore adopt doc mySecurity myDataStore sync persistentDoc Security User access to the LRs is provided by a security mechanism of users and groups similar to those on an operating system When users create save LRs into Oracle they specify reading and writing access rights for users from their group and other users For example LRs created by one user group can be made read only to others so they can use the data but not modify it The access modes are e others read none 11 Alternatively a string giving the document source may be provided SS SE a e OO SS a ee eee ee Se ee e group modify read none e owner modify read If needed ownership can be transferred from one user to another Users groups and LR permissions are administered in a special administration tool by a privileged user For more details see chapter 10 When you want to restore a document or other LR from a data store you make the same createResource call to the Facto
97. ed by selecting a group in the list of groups and right clicking on it to see the group manipulation menu This menu allows the following actions Create new group shows a dialog where the name of the new group must be specified Delete group delete the currently selected group Add user shows a dialog displaying all available users Select one to add to the group Remove user in the given dialog choose the user to be removed Rename group choose another name for the selected group All changes are automatically written to the Oracle database 10 2 3 The API In order to work with users and groups programmatically you need to use an access con troller which is the class that provides the connection to the Oracle database The access controller needs to be closed before application exit Once the connection is established you need to create a session by proving the login de tails of the user user name password and group Any user who can login can use the 1See the latest API documentation online at http gate ac uk gate doc javadoc index html User and group API is located in the gate security package iw f MEM PATO Seto pe fil i a MMA Rachie scm os n dS VY ELULE NAL RE SES ivi accessor methods for users groups but only the ADMIN user has priviliges to modify the data The way to check whether the logged in user has the right to modify data is to use the isPriviligedSession method see below If a mutator method
98. ee a hala TA Parto Speech tagger vaio SE ede BR ee ee ee A 7 5 Semantic Tagger Slate o Be a aa 7 6 Orthographic Coreference OrthoMatcher o o o o o 7 7 Pronominal Coreference aa e da a a a a bale 7 8 A Walk Through Example del ds a More CREOLE 8 1 Document Reset e esie Seti tas E es AAA AAA S 2 Verb Group Chunker sas diore maned a a e hw ele ea ee A 8 3 Noun Group Chunker aa aaa a SA Onto Text Gazette o ae lt e dc A Se 8 5 Flexible Exporter 3 a a a a a 8 6 Annotation Set Transfer goed a e de OE ee A Performance Evaluation of Language Analysers 9 1 The Annotation DUE Asoc ye o Barco ates ie Bo Fok Ye e Ee WS 56 57 58 58 59 60 60 62 65 65 66 66 66 T1 84 86 90 91 93 95 97 99 100 103 104 104 104 104 105 111 114 115 115 115 117 119 119 120 ALLAN OLEO NEU EU 9 2 The six annotation relations explained 8 01 Benchmarking tool aria a eo a aie et ay ae 9 4 Metrics fo r Evaluation in Information Extraction 10 Users Groups and LR Access Rights 10 1 Java serialisation and LR access rights 0 o 10 2 Oracle Datastore and LR access rights o Appendices A Design Notes A l Patterns A 2 Exception Handling te saten gaite a Ra AR eee B JAPE Implementation B 1 Formal Description of the JAPE Grammar B 2 Relation t O ryn aaae e ee A ec a ED ete Be e
99. een curly braces and gives a conjunction of annotation attribute value specifiers which have to match at a particular point in the an notation graph A complex constraint specification appears within round brackets and may be bound to a label with the operator the label then becomes available in the RHS for access to the annotations matched by the complex constraint Complex constraints can also have Kleene operators applied to them A sequence of constraints represents a sequential conjunction disjunction is represented by separating constraints with Converted to the format accepted by the JavaCC LL parser generator the most significant fragment of the CPSL grammar as described by Appelt based on an original specification from a TIPSTER working group chaired by Boyan Onyshkevych goes like this constraintGroup gt patternElement patternElement patternElement gt constraint constraint 3 constraintGroup kleene0p binding Here the first line of patternElement is a basic constraint the second a complex one ERRE AER A A ae ee ae en iota MultiPhaseTransducer lt multiphase gt lt ident gt SinglePhaseTransducer lt phases gt lt ident gt lt EOF gt SinglePhaseTransducer lt phase gt lt ident gt lt input gt lt ident gt lt option gt lt ident gt lt assign gt lt ident gt C Rule MacroDe
100. efault parameters ProcessingResource tagger ProcessingResource Factory createResource gate creole POSTagger params Note that if the resource created here had any parameters that were both mandatory and had no default value the createResource call would throw an exception In this case all the information needed to create a tagger is available in default values given in the tagger s XML definition lt RESOURCE gt 10 This method is not part of the beans spec TAREA O TAREA O E UNES EE Se ee eee AE SS ee lt NAME gt ANNIE POS Tagger lt NAME gt lt COMMENT gt Mark Hepple s Brill style POS tagger lt COMMENT gt lt CLASS gt gate creole POSTagger lt CLASS gt lt PARAMETER NAME document COMMENT The document to be processed RUNTIME true gt gate Document lt PARAMETER gt lt PARAMETER NAME rulesURL DEFAULT gate creole heptag ruleset COMMENT The URL for the ruleset file OPTIONAL true gt java net URL lt PARAMETER gt lt RESOURCE gt Here the two parameters shown are either runtime parameters which are set before a PR is executed or have a default value in this case the default rules file is distributed with GATE itself When creating a Document however the URL of the source for the document must be provided For example URL u new URL http gate ac uk hamish FeatureMap params Factory newFeatureMap params put sourceUrl u Document doc Document Factory creat
101. eld event in which the athletes for want of a better word all lay down in beds just beyond where the javelins land and the first one to fall asleep and not wake up for three hours would win gold I for one would be interested in seeing what kind of personality would be suited to sleeping in a competitive environment Life is a mystery to be lived not a problem to be solved Round Ireland with a Fridge Tony Hawks 1998 pp 119 147 179 This chapter documents GATE s model of corpora documents and annotations on docu ments Section 5 1 describes the simple attribute value data model that corpora documents and annotations all share Section 5 2 section 5 3 and section 5 4 describe corpora docu ments and annotations on documents respectively Section 5 5 describes GATE s support for diverse document formats and section 5 6 describes facilities for XML input output 5 1 Features Simple Attribute Value Data GATE has a single model for information that describes documents collections of documents corpora and annotations on documents based on attribute value pairs Attribute names are strings values can be any Java object The API for accessing this feature data is Java s Map interface part of the Collections API 65 SS SE O OO UNES EE AE A AE SS ee 5 2 Corpora Sets of Documents plus Features A Corpus in GATE is a Java Set whose members are Documents Both Corpora and Docu ments are types of LanguageResour
102. ent and the disadvantage that it creates a complete source tree and build structure for the component If you already have a source tree you will need to chop out the bits you need from the new tree in this case GoldFish java and resource xml and copy it into your existing one 2 8 F Instantiate CREOLE Resources This section describes how to create CREOLE resources as objects in a running Java virtual machine This process involves using GATE s Factory class and in the case of LRs may also involve using a DataStore CREOLE resources are Java Beans creation of a resource object involves using a default constructor then setting parameters on the bean then calling an init method The Factory takes care of all this makes sure that the GUL is told about what is happenning when GUI components exist at runtime and also takes care of restoring LRs from DataStores So a programmer using GATE should never call the constructor of a resource always use the Factory The valid parameters for a resource are described in the resource s section of its creole xml file see section 2 10 Creating a resource via the Factory involves passing values for any create time parameters that require setting to the Factory s createResource method If no parameters are passed the defaults are used So for example the following code creates a default ANNIE part of speech tagger FeatureMap params Factory newFeatureMap empty map d
103. er to do this we will have to find a subset of annotations that match the restrictions for moving to state 2 or state 3 In a classical algorithm what we would do is to try to match the annotations against the restrictions 1 2 this will return a boolean value and a set of bindings and then we will try the matching against the restrictions 1 3 this means that we will try to match the restrictions in the common part twice Because of the probable structure of the FSM transition graph there will be a lot of transitions starting from the same node which means that may be a lot of conditions checked more than one times What can we do to improve this We need a way to combine all the restrictions associated to all outgoing arcs of a state see Fig B 4 I lt a a a gt Exa al A gt lisa A en gt Matcher 2 gt D NAA l lt a a a gt lt a a A gt l lt a a a gt Figure B 4 A combined matching process One way to do the combined matching is to pre process the DFSM and to convert all transitions to matchers as in Fig B 4 This could be done using the following algorithm e Input A DFSM e Output A DFSM with compound restrictions checks e for each state s of the DFSM 1 collect all the restrictions in the labels of the outgoings arcs from s in the DFSM transition graph Note these restrictions are either of form Type t ti amp amp Attri Value 2 Group all
104. est trick So we looked into using XML as source for this document and using something like DocBook to translate it into the PDF and HTML versions that we wanted to provide for printing and web viewing Nice ideas but our conclusion was that they re not really ready right now So in the end it was good old TEX and TeX4HT for the HTML production Thank you Don Knuth Leslie Lamport and Eitan Gurari http www docbook org 2http ww cis ohio state edu gurari TeX4ht mn html 171
105. eters 9 2 The six annotation relations explained Coextensive Two annotations are coextensive if they hit the same span of text in a document Basically both their start and end offsets are equal Overlaps Two annotations overlap if they share a common span of text TAREA O ARE Se RO AE ee ee ee SS IT ia Compatible Two annotations are compatible if they are coextensive and if the features of one usually the ones from the key are included in the features of the other usually the response Partially Compatible Two annotations are partially compatible if they overlap and if the features of one usually the ones from the key are included in the features of the other response Missing This applies only to the key annotations A key annotation is missing if either it is not coextensive or overlapping orif one or more features are not included in the response annotation Spurious This applies only to the response annotations A response annotation is spurious if either it is not coextensive or overlapping or if one or more features from the key are not included in the response annotation 9 3 Benchmarking tool The benchmarking tool differs from the AnnotationDiff in that it enables evaluation to be carried out over a whole corpus rather than a single document It also enables tracking of the system s performance over time The tool can be run in either GUI mode or standalone mode For more information on how to run t
106. ework and graphical devel opment environment as a tool for robust NLP applications Bontcheva et al 02b NLIS 2002 discusses how GATE can be used to create HLT mod ules for use in information systems Tablan et al 02 LREC 2002 describes GATE s enhanced Unicode support Maynard et al 02c ACL 2002 Summarisation Workshop describes using GATE to build a portable IE based summarisation system in the domain of health and safety Maynard et al 02d Nordic Language Technology describes various Named Entity recognition projects developed at Sheffield using GATE Maynard et al 02a AIMSA 2002 describes the adaptation of the core ANNIE modules within GATE to the ACE Automatic Content Extraction tasks Maynard et al 02b JNLE describes robustness and predictability in LE systems and presents GATE as an example of a system which contributes to robustness and to low overhead systems development Bontcheva et al 02c Dimitrov et al 02 and Dimitrov 02 TALN 2002 DAARC 2002 MSc thesis describe the shallow named entity coreference modules in GATE the orthomatcher which resolves pronominal coreference and the pronoun resolution module IShttp gate ac uk gate doc javadoc Mhttp gate ac uk gate doc java2html IShttp gate ac uk gate doc usecases html l6http n1lp shef ac uk http www dcs shef ac uk hamish Lelntro html IShttp www dcs shef ac uk hamish IE 19http www dcs shef ac uk nlp extractio
107. express it in numbers your knowledge is of a meager and unsatisfactory kind it may be the beginning of knowledge but you have scarcely in your thoughts advanced to the stage of science Kelvin Not everything that counts can be counted and not everything that can be counted counts Einstein GATE provides two useful tools for automatic evaluation the AnnotationDiff tool and the Benchmarking Tool These are particularly useful not just as a final measure of performance but as a tool to aid system development by tracking progress and evaluating the impact of changes as they are made The evaluation tool AnnotationDiff enables automated performance measurement and visualisation of the results while the benchmarking tool enables the tracking of a system s progress and regression testing 9 1 The AnnotationDiff Tool The AnnotationDiff tool enables two sets of annotations on a document to be compared in order either to compare a system annotated text with a reference hand annotated text or to compare the output of two different versions of the system or two different systems For each annotation type figures are generated for precision recall F measure and false positives Each of these can be calculated according to 3 different criteria strict lenient and average The reason for this is to deal with partially correct responses in different ways e The Strict measure considers all partially correct responses as incorrect spuriou
108. f Rule lt rule gt lt ident gt lt priority gt lt integer gt LeftHandSide gt RightHandSide MacroDef lt macro gt lt ident gt PatternElement Action LeftHandSide ConstraintGroup ConstraintGroup PatternElement lt bar gt PatternElement PatternElement lt ident gt BasicPatternElement ComplexPatternElement BasicPatternElement lt leftBrace gt Constraint lt comma gt Constraint lt rightBrace gt lt string gt ComplexPatternElement lt leftBracket gt ConstraintGroup lt rightBracket gt lt kleeneOp gt lt colon gt lt ident gt lt integer gt Constraint lt pling gt lt ident gt lt period gt lt ident gt lt equals gt AttrVal AttrVal lt string gt lt ident gt lt integer gt lt floatingPoint gt lt bool gt RightHandSide Action lt comma gt Action Action NamedJavaBlock AnonymousJavaBlock AssignmentExpression lt ident gt NamedJavaBlock lt colon gt lt ident gt lt leftBrace gt ConsumeBlock AnonymousJavaBlock lt leftBrace gt ConsumeBlock AssignmentExpression lt colon gt lt colonplus gt lt ident gt lt period gt lt ident gt lt assign gt lt leftBrace gt lt ident gt lt assign gt AttrVal lt colon gt lt ident gt lt period gt lt ident gt lt period gt lt ident gt lt comma gt lt rightBrace gt
109. f the application file This allows the creation and deployment of portable applications by keeping the application file and the resource files used by the application together The easiest way of deploying a portable GATE application is to store the application file and the application resources under the same top directory which will become the deployment unit 2 18 D F Perform Evaluation with the Annotation Diff tool Section 9 describes the theory behind this tool TAREA O OO E UNES AE A ee SS IT 2 18 1 GUI The annotation tool is activated by selecting it from the Tools menu at the top of the window It will appear in a new window Select the key and response documents to be used note that both must have been previously loaded into the system the annotation sets to be used for each and the annotation type to be evaluated Note that the tool automatically intersects all the annotation types from the selected key annotation set with all types from the response set On a separate note you can perform a diff on the same document between two different annotation sets One annotation set could contain the key type and another could contain the response one After the type has been selected the user is required to decide how the features will be compared It is important to know that the tool compares them by analyzing if features from the key set are contained in the response set It checks for both the feature name and feature val
110. fails to appear in a visible area of the screen at which point the rest of the GUI waits for you to do something intelligent with the dialogue box while you wait for the GUI to do something This is an excellent feature for those without tight deadlines to meet and the best solution is to stop work and go home for a long while Alternatively you can play hunt the dialogue box This feature is available totally free of charge 2 26 D Stop GUI Crashing on Linux On some configurations of Red Hat 7 0 with JDK 1 3 1 the GUI crashes on startup The solution is to limit the initial stack size prior to launch ulimit s 2048 2 27 D Stop GATE Restoring GUI Sessions Options GATE will remember GUI options and the state of the resource tree when it exits The options are saved by default the session state is not saved by default This default behaviour can be changed from the Advanced tab of the Configuration choice on the Options menu If a problem occurs and the saved data prevents GATE from starting you can fix it by deleting the configuration and session data files These are stored in your home directory and are called gate xml and gate sesssion or gate xml and gate sesssion depending on platform On Windoze your home is TAREA O OO E UNES AE ee eee ee SS IT 95 98 NT Windows Directory profiles username 2000 XP Windows Drive Documents and Settings username 2 28 Work with Unicode GATE provides vari
111. ficial Intelligence ECAI 98 pages 185 189 Brighton U K 1998 Tablan et al 02 V Tablan C Ursu K Bontcheva H Cunningham D Maynard O Hamza T McEnery P Baker and M Leisher A unicode based environment for creation and use of language resources In Proceedings of 3rd Language Resources and Eval uation Conference 2002 Unicode Consortium 96 Unicode Consortium The Unicode Standard Version 2 0 Addison Wesley Reading MA 1996 van Rijsbergen 79 C van Rijsbergen Information Retrieval Butterworths London 1979 Yourdon 89 E Yourdon Modern Structured Analysis Prentice Hall New York 1989 Yourdon 96 E Yourdon The Rise and Resurrection of the American Programmer Prentice Hall New York 1996 Colophon Formal semantics henceforth FS at least as it relates to computational lan guage understanding is in one way rather like connectionism though without the crucial prop Sejnowski s work 1986 is widely believed to give to the latter both are old doctrines returned like the Bourbons having learned nothing and forgotten nothing But FS has nothing to show as a showpiece of success after all the intellectual groaning and effort On Keeping Logic in its Place in Theoretical Issues in Natural Language Pro cessing ed Wilks Yorick Wilks 1989 p 130 We wanted to be modern we wanted to make the XML people feel like progress is indeed happening we wanted to update our CVs with the lat
112. from the start end nodes and Features Integers are used as the annotation Ids The features are shown in the form name value The first example shows a single sentence and the result of three annotation procedures to kenization with part of speech assignment name recognition and sentence boundary recog nition Each token has a single feature its part of speech pos using the tag set from the University of Pennsylvania Tree Bank each name also has a single feature indicating the type of name person company etc Annotations will typically be organized to describe a hierarchical decomposition of a text A simple illustration would be the decomposition of a sentence into tokens A more complex case would be a full syntactic analysis in which a sentence is decomposed into a noun phrase and a verb phrase a verb phrase into a verb and its complement etc down to the level of individual tokens Such decompositions can be represented by annotations on nested sets http www itl nist gov iaui 894 02 related_projects tipster e A e e Mn e il o eS EO SE a A SS ar 0 5 10 15 20 Type fo f hom 2 token 6 3 pos VBD__ sia 22 s S 6 name 5 mame type person_ 5 77 semtencs o a gt gt gt gt Table 5 1 Result of annotation on a single sentence 5 1 1 2 2 5 Cyndi savored the soup 0 5 10 15 20 Annotations Features pos NP pos VBD oo po 2 token e e gt 3 token a
113. g in HashMaps will be faster than a search in a Finite State Machine FSM Here follows a description of the algorithm that lies behind this implementation Every phrase i e every list entry is separated into several parts The parts are determined by the whitespaces lying among them e g the phrase form is emptiness has three parts form is amp emptiness There is also a list of HashMaps mapsList which has as many elements as the longest in terms of count of parts phrase in the lists So the first part of a phrase is placed in the first map The first part space second part is placed in the second map etc The full phrase is placed in the appropriate map and a reference to a Lookup object is attached to it On first sight it seems that this algorithm is certainly much more memory consuming than a finite state machine FSM with the parts of the phrases as transitions but this is actually not so important since the average length of the phrases in parts in the lists is 1 1 On the other hand one advantage of the algorithm is that although unconventional on average it takes four times less memory and works three times faster than an optimized FSM implementation The lookup part is implemented in execute so a lot of tokenization takes place there After defining the candidates for phrase parts we build a candidate phrase and try to look it up in the maps in which map again depends on the count of parts in the curre
114. g to the main Gate window if any features have been added otherwise these will be lost To add more than one feature click on Add Feature after each feature has been added 2 15 3 Saving the test data The data can either be dumped out as a file see Section 2 24 or saved in a data store see Section 2 16 2 16 Dj Save and Restore LRs in Data Stores To save a text in a data store a new data store must first be created if one does not already exist Create a data store by right clicking on Data Store in the left hand pane and select the option Create Data Store Select the data store type you wish to use Create a directory to be used as the data store note that the data store is a directory and not a file Save the text to the data store by right clicking on the document name and selecting the Save to option giving the name of the datastore created earlier TAE EE O TIENE UNES E AE ee ee ee SS ee To load a document from a data store do not try to load it as a language resource Instead open the data store by right clicking on Data Store in the left hand pane select Open Data Store and choose the data store to open The filenames contained in the data store should appear in the left hand pane Double click on a file to open it Once opened the file will then appear under Language Resources in the left hand pane Double click on this file to view it in the main window It can be treated in the same way as
115. g two rules for location could potentially match the same text Rule Location1 Priority 25 Lookup majorType loc_key Lookup minorType pre SpaceToken Lookup majorType location SpaceToken Lookup majorType loc_key Lookup minorType post locName gt locName Location kind location rule Locationi Rule GazLocation Priority 20 Lookup majorType location location gt location Name kind location rule GazLocation Assume we have the text China sea that China is defined in the gazetteer as location and that sea is defined as a loc_key of type post In this case rule Location would apply because it matches a longer region of text starting at the same point China sea as opposed to just China Now assume we just have the text China In this case both rules could be fired but the priority for Locationl is highest so it will take precedence In this case since both rules produce the same annotation so it is not so important which rule is fired but this is not always the case One important point of which to be aware is that prioritisation only operates within a single grammar Although we could make priority global by having all the rules in a single grammar this is not ideal due to other considerations Instead we currently combine all the rules for each entity type in a single grammar An index file ma
116. ge Processing In Proceedings of the 5th Conference on Ap plied Natural Language Processing ANLP 97 March 1997 http xxx lanl gov abs cs CL 9702005 Cunningham et al 98a H Cunningham W Peters C McCauley K Bontcheva and Y Wilks A Level Playing Field for Language Resource Evaluation In Workshop on Distributing and Accessing Lexical Resources at Conference on Language Resources Evaluation Granada Spain 1998 Cunningham et al 98b H Cunningham M Stevenson and Y Wilks Implementing a Sense Tagger within a General Architecture for Language Engineering In Proceedings of the Third Confer ence on New Methods in Language Engineering NeMLaP 3 pages 59 72 Sydney Australia 1998 Cunningham et al 99 H Cunningham R Gaizauskas K Humphreys and Y Wilks Experience with a Language Engineering Architecture Three Years of GATE In Proceedings of the AISB 99 Workshop on Reference Architectures and Data Standards for NLP Edin burgh April 1999 The Society for the Study of Artificial Intelligence and Simulation of Behaviour Cunningham et al 00a H Cunningham K Bontcheva W Peters and Y Wilks Uniform lan guage resource access and distribution in the context of a General Architec ture for Text Engineering GATE In Proceedings of the Workshop on Ontolo gies and Language Resources OntoLex 2000 Sozopol Bulgaria September 2000 http gate ac uk sale ontolex ontolex ps Cunningham et al 00b
117. ge of the items identified It is sometimes used as an alternative to precision Recall measures the number of correctly identified items as a percentage of the total number of correct items In other words it measures how mnay of the items that should have been identified actually were identified regardless of how many spurious identifications were made The higher the recall rate the better the system is at not missing correct items iw t MEM PATO Seto pe fil i a MMA a os A VY LULE NAL RE SES tat Clearly there must be a tradeoff between precision and recall for a system can easily be made to achieve 100 precision by identifying nothing and so making no mistakes in what it identifies or 100 recall by identifying everything and so not missing anything The F measure van Rijsbergen 79 is often used in conjunction with Precision and Recall as a weighted average of the two if the weight is set to 0 5 precision and recall are deemed equally important False positives are a useful metric when dealing with a wide variety of text types because it is not dependent on relative document richness in the same way that precision is When comparing different systems on the same document set relative document richness is unimportant because it is equal for all systems When comparing a single system s performance on different documents however it is much more crucial because if a particular document type has a significantly different n
118. gs of the Fourth Discourse Anaphora and Anaphor Resolution Colloquium DAARC Lisbon 2002 forthcoming Frakes amp Baeza Yates 92 W Frakes and R Baeza Yates editors Information retrieval data structures and algorithms Prentice Hall New York Englewood Cliffs N J 1992 Gaizauskas amp Wilks 98 R Gaizauskas and Y Wilks Information Extraction Beyond Document Retrieval Journal of Documentation 54 1 70 105 1998 Gaizauskas et al 96a R Gaizauskas P Rodgers H Cunningham and K Humphreys GATE User Guide http www dcs shef ac uk nlp gate 1996 Gaizauskas et al 96b R Gaizauskas H Cunningham Y Wilks P Rodgers and K Humphreys GATE an Environment to Support Research and Development in Natural Language En gineering In Proceedings of the 8th IEEE International Conference on Tools with Artificial Intelligence ICTAI 96 Toulouse France October 1996 Gamb ck amp Olsson 00 B Gamb ck and F Olsson Experiences of Language Engineering Algorithm Reuse In Second International Conference on Language Resources and Evaluation LREC pages 155 160 Athens Greece 2000 A LLAVE NANO NS ivVvV Gazdar amp Mellish 89 G Gazdar and C Mellish Natural Language Processing in Prolog Addison Wesley Reading MA 1989 Grishman 97 R Grishman TIPSTER Architecture Design Document Version 2 3 Technical re port DARPA 1997 http ww itl nist gov div894 894 02 related projects tipster
119. hat are selected i e are visible in the table at the bottom of the viewer will be included in the output dump This is the best way to use the system to add markup lMGorey details features of annotations and documents in GATE may be any virtually any Java object serialising arbitrary binary data to XML is not simple instead we serialise them as strings and therefore they will be re loaded as strings SS SE O AEREO UNES EE AE A ee SS ee based on some analysis process select those annotations in the document viewer save preserving format and you will have a file identical to the original source document with just the annotations you selected added By default the added annotations will contain no feature data if you want the process to also dump features set the Include annotation features option in the advanced options dialogue see section 2 5 Note that GATE s model of annotation allows graph structures which are difficult to represent in XML XML is a tree structured representation format During the dump process annotations that cross each other in ways that can t be represented straightforwardly in XML will be discarded and a warning message printed 2 25 D Stop GUI Freezing on Linux There is a problem with some versions of Linux that causes the GUI to appear to freeze The problem occurs when you take some action like loading a resource or browsing for a file that pops up a dialogue box This box sometimes
120. he document s features gt lt GateDocumentFeatures gt lt Feature gt lt Name className java lang String gt MimeType lt Name gt lt Value className java lang String gt text plain lt Value gt lt Feature gt lt Feature gt lt Name className java lang String gt gate SourceURL lt Name gt lt Value className java lang String gt file G tmp example txt lt Value gt lt Feature gt lt GateDocumentFeatures gt lt The document content area with serialized nodes gt lt TextWithNodes gt lt Node id 0 gt A TEENAGER lt Node id 11 gt yesterday lt Node id 20 gt accused his parents of cruelty by feeding him a daily diet of chips which sent his weight ballooning to 22st at the age of 12 lt Node id 146 gt lt Node id 147 gt lt TextWithNodes gt lt The default annotation set gt lt Annot ationSet gt iw f MEM PATO Seto pe fil i a MMA a os A VY LULE NAL RE SES ro lt Annotation Type Date StartNode 11 EndNode 20 gt lt Feature gt lt Name className java lang String gt rule2 lt Name gt lt Value className java lang String gt DateOnlyFinal lt Value gt lt Feature gt lt Feature gt lt Name className java lang String gt rule1 lt Name gt lt Value className java lang String gt GazDateWords lt Value gt lt Feature gt lt Feature gt lt Name className java lang String gt kind lt Name gt lt Value className java lang String gt date lt Value g
121. he quoted speech submodule One important difference from the resolution process of other pronouns is that the context is not measured in sentences but depends solely on the quote span Another difference is that the context is not contiguous the quoted fragment itself is excluded from the context because it is unlikely that an antecedent for I me etc appears there The context itself consists of e the part of the sentence where the quoted fragment originates that is not contained in the quote i e the text prior to the quote e the part of the sentence where the quoted fragment ends that is not contained in the quote i e the text following the quote e the part of the sentence preceding the sentence where the quote originates which is not included in other quote It is worth noting that contrary to other pronouns the antecedent for I me my and myself is most often cataphoric or if anaphoric it is not in the same sentence with the quoted fragment The resolution algorithm consists of the following steps e Locate the quoted fragment description that contains the pronoun If the pronoun is not contained in any fragment then return without proposing an antecedent TAREA OO E UNES EE AE ee ee ee SS ee LALA e Inspect the context for the quoted fragment as defined above for candidate an tecedents Candidates are considered annotations of type Pronoun or annotations of type Token with features category PRP string
122. he tool see 2 19 The tool requires a clean version of a corpus with no annotations and an annotated reference corpus First of all the tool is run in generation mode to produce a set of texts annotated by the system These texts are stored for future use The tool can then be run in three ways 1 comparing the stored processed set with the human annotated set 2 comparing the current processed set with the human annotated set 3 default mode comparing the stored processed set with the current processed set and the human annotated set In each case performance statistics will be output for each text in the set and overall statistics for the entire set In the default mode information is also provided about whether the figures have increased or decreased in comparison with the annotated set The processed set can be updated at any time by rerunning the tool in generation mode with the latest version of the system resources Furthermore the system can be run in verbose mode where for each P and R figure below a certain threshold set by the user the non coextensive Se ae ee o a gt ee ee il N ievV annotations and their corresponding text will be displayed The output of the tool is written to an HTML file in tabular form for easy viewing of the results see Figure 9 2 ABC19980430 1830 0858 sgm Annotation Type Precision MISSING ANNOTATIONS in the automatic texts ABC 2049 265 Annotation type Precision incre
123. her evaluation e Evaluate each candidate with the best candidate so far If the two candidates are anaphoric for the pronoun then choose the one that appears closer The same holds for the case where the two candidates are cataphoric relative to the pronoun If one is anaphoric and the other is cataphoric then choose the former even if the latter appears closer to the pronoun SS SE O OO Se SO Se Ee A eee ee SS IT LIV Resolution of it its itself This set of pronouns also shares many common characteristics The resolution process con tains certain differences with the one for the previous set of pronouns Successful resolution for it its itself is more difficult because of the following factors e There is no gender compatibility restriction In the case when there are several can didates in the context the gender compatibility restriction is very useful for rejecting some of the candidates When no such restriction exists and with the lack of any syn tactic or ontological information about the entities in the context the recency factor plays the major role for choosing the best antecedent e The number of nominal antecedents i e entities that are referred not by name is much higher compared to the number of such antecedents for she he etc In this case trying to find antecedent only amongst named entities degrades the precision a lot Resolution of I me my myself Resolution of these pronouns is dependent on the work of t
124. ic FSM from the java objects resulted from the parsing process This FSM will have one initial state and a set of final states each of them being associated to one rule this way we know what RHS we have to execute in case of a match The nondeterministic FSM will also have empty transitions arcs labeled with nil In order to build this FSM we will need to implement a version of the algorithm used to convert regular expressions in NFAs Finally this nondeterministic FSM will have to be converted to a deterministic one The deterministic FSM will have more states in the worst case s where s is the number of states in the nondeterministic one this case is very improbable but will be more efficient because it will not have to backtrack Let NFSM be the nondeterministic FSM and DFSM the deterministic one The issues that have to be addressed are The NFSM will basically be a big OR This means that it will have an initial state from which empty transitions will lead to the sub FSMs associated to each rule see Fig B 2 When the NFSM is converted to a DFSM the initial state will be the set containing all the initial states of the FSMs associated to each rule From that state we will have to compute the possible transitions For this the classical algorithm requires us to check for each possible a a id ity RHS1 RHS2 Y Initial state lt gt Empty transition Final state Figure B 2 A nondeterministic FSM input sy
125. id e ir os n dS VY ELULE NAL RE SES UD e The gender of each person in the sentence is identified and stored in a global data structure It is possible that the gender information is missing for some entities for example if only the person family name is observed then the Named Entity transducer will be unable to deduce the gender In such cases the list with the matching entities generated by the OrhtoMatcher is inspected and if some of the orthographic matches contains gender information it is assigned to the entity being processed The identified pleonastic it occurrences are stored in a separate list The Pleonastic It annotations generated from the pleonastic submodule are used for the task For each quoted text fragment identified by the quoted text submodule a special structure is created that contains the persons and the 3rd person singular pronouns such as he and she that appear in the sentence containing the quoted text but not in the quoted text span i e the ones preceding and succeeding the quote Pronoun resolution This task includes the following subtasks etrieving all the pronouns in the document Pronouns are represented as annotations of type Token with feature category having value PRP or PRP The former classifies possessive adjectives such as my your etc and the latter classifies personal reflexive etc pronouns The two types of pronouns are combined in one list and s
126. ima stops using the GATE development environment and works instead on embedding the new components in her email application This application is written in Java so embedding is very easy the two GATE JAR files are added to the project CLASSPATH the new components are placed on a web server and with a little code to do initialisation loading of components and so on the job is finished in half a day the code to talk to GATE takes up only around 150 lines of the eventual application most of which is just copied from the example in the sheffield examples StandAloneAnnie class Because Fatima is worried about Cyberdyne s unethical policy of developing Skynet to help the large corporates of the West strengthen their strangle hold over the World she wants to get a job as an academic instead so that her conscience will only have to cope with the torture of students as opposed to humanity She takes the accuracy measures that she has attained for her system and writes a paper for the Journal of Nasturtium Logarithm Encitement describing the approach used and the results obtained Because she used GATE for development she can cite the repeatability of her experiments and offer access to example binary versions of her software by putting them on an external web server And everybody lived happily ever after 1 4 Structure of the Book The material presented in this book ranges from the conceptual e g what is software architecture
127. in jape is used to define which grammars should be used and in which order they should be fired aS SEE O TAREA Se OS Ee A ee SS IT 6 3 Useful tricks Although the JAPE language has some limitations as to how rules and patterns can be expressed there are some useful tricks to overcome these problems e Using priority to resolve ambiguity If the Appelt style of matching is selected rule priority operates in the following way 1 Length of rule a rule matching a longer pattern will fire first 2 Explicit priority declaration Use the optional Priority function to assign a rank ing The higher the number the higher the priority If no priority is stated the default is 1 3 Order of rules In the case where the above two factors do not distinguish between two rules the order in which the rules are stated applies Rules stated first have higher priority Because priority can only operate within a single grammar this can be a problem for dealing with ambiguity issues One solution to this is to create a temporary set of annotations in initial grammars and then manipulate this temporary set in one or more later phases for example by converting temporary annotations from different phases into permanent annotations in a single final phase See the default set of grammars for an example of this e Negative operator A negative operator cannot be specified as such One solution to this is to create a negative rule which ha
128. inalContent COMMENT Should the document preserve the original content DEFAULT false gt java lang Boolean lt PARAMETER gt lt PARAMETER NAME collectRepositioningInfo COMMENT Should the document collect repositioning information DEFAULT false gt java lang Boolean lt PARAMETER gt lt ICON gt 1r gif lt ICON gt lt RESOURCE gt lt RESOURCE gt lt NAME gt Document Reset PR lt NAME gt lt CLASS gt gate creole annotdelete AnnotationDeletePR lt CLASS gt lt COMMENT gt Document cleaner lt COMMENT gt lt PARAMETER NAME document RUNTIME true gt gate Document lt PARAMETER gt TAREA O TAE UNES AE A ee ee SS ee lt PARAMETER NAME annotationTypes RUNTIME true OPTIONAL true gt java util ArrayList lt PARAMETER gt lt RESOURCE gt Parameters may be optional and may have default values and may have comments to describe their purpose which is displayed by the GUI during interactive parameter setting Some PR parameters are execution time RUNTIME some are initialisation time E g at execution time a doc is supplied to a language analyser at initilisation time a grammar may be supplied to a language analyser Each parameter has a type which may be the type of another resource or a Java built in Attributes of parameters NAME name of the property that the parameter refers to if supplied it will change the name that the initialisation routines assume are available to get set on the resource which are normal
129. ination with the comments in the relevant grammar files chapter 6 also provides in formation about designing new grammar rules and tips for ensuring maximum processnig speed C 1 Main jape This file contains a list of the grammars to be used in the correct processing order The ordering of the grammars is crucial because they are processed in series and later grammars 155 ci VINA ARC a Re Aeron ere REE a eS PR a er A i Vv may depend on annotations produced by earlier grammars The default grammar consists of the following phases e first jape e firstname jape e name jape e name_post jape e date_pre jape e date jape e reldate jape e number jape e address jape e url jape e identifier jape e jobtitle jape e final jape e unknown jape e name_context jape e org_context jape e loc_context jape e clean jape C 2 first jape This grammar must always be processed first It can contain any general macros needed for the whole grammar set This should consist of a macro defining how space and control characters are to be processed and may consequently be different for each grammar set depending on the text type Because this is defined first of all it is not necessary to restate this in later grammars This has a big advantage it means that default grammars can be used for specialised grammar sets without having to be adapted to deal with e g different ci VINA ANC eRe Aeron ere REE ae eS OE et A ive tre
130. ing of an application so that a document is reset before an application is rerun on that document 8 2 Verb Group Chunker The rule based verb chunker is based on a number of grammars of English Cobuild 99 Azar 89 We have developed 68 rules for the identification of non recursive verb groups The rules cover finite is investigating non finite to investigate participles investi gated and special verb constructs is going to investigate All the forms may include adverbials and negatives The rules have been implemented in JAPE The finite state anal yser produces an annotation of type VG with features and values that encode syntactic information type tense voice neg etc The rules use the output of the POS tagger as well as information about the identity of the tokens e g the token might is used to identify modals The grammar for verb group identification can be loaded as a Jape grammar into the GATE architecture and can be used in any application the module is domain independent 8 3 Noun Group Chunker The NG chunker is based on the algorithm used in the POS tagger described in Hepple 00a We have developed a transformation based chunking procedure that learns how to identify non recursive noun chunks in a text The algorithm is trained on the Wall Street Journal WSJ Corpus The original parsed WSJ was re tagged in order to include information about non recursive nou
131. instruc tions for completing common tasks with GATE organised in a FAQ style details and the reasoning behind the various aspects of the system are omitted in this chapter so where more information is needed refer to later chapters The structure of the book as a whole is detailed in section 1 4 below 1 2 Context GATE can be thought of as a Software Architecture for Language Engineering Cunningham 00 Software Architecture is used rather loosely here to mean computer infrastructure for soft ware development including development environments and frameworks as well as the more usual use of the term to denote a macro level organisational structure for software systems Shaw amp Garlan 96 Language Engineering LE may be defined as the discipline or act of engineering software systems that perform tasks involv ing processing human language Both the construction process and its outputs are measurable and predictable The literature of the field relates to both appli cation of relevant scientific results and a body of practice Cunningham 99a The relevant scientific results in this case are the outputs of Computational Linguistics Nat ural Language Processing and Artificial Intelligence in general Unlike these other disciplines LE as an engineering discipline entails predictability both of the process of constructing LE based software and of the performance of that software after its completion and deployme
132. ir pos DT wo f ao AB 5 13 14 17 18 22 pos NN 27 e rame f0 fs ECTS 7 sentence 0 23 constituents 1 2 3 4 5 Table 5 2 Result of annotations including parse information of spans Both of these are illustrated in the second example which is an elaboration of our first example to include parse information Each non terminal node in the parse tree is represented by an annotation of type parse In most cases the hierarchical structure could be recovered from the spans However it may be desirable to record this structure directly through a constituents feature whose value is a sequence of annotations representing the immediate constituents of the initial annota tion For the annotations of type parse the constituents are either non terminals other annotations in the parse group or tokens For the sentence annotation the constituents feature points to the constituent tokens A reference to another annotation is represented in the table as Annotation Id for example 3 represents a reference to annotation 3 Where the value of an feature is a sequence ofitems these items are separated by commas No special operations are provided in the current architecture for manipulating constituents Lv AER D MAB A e a o il VY ELULE NAL RE SES ey 0 5 10 15 20 125 130 135 40 145 50 55 60 65 70 75 80 85 90 95 100 105 110 115 120 125 130 135
133. ished to place it again among the mass of anonymous and forgotten volumes lined up on the shelf But perhaps I think to console myself perhaps reading like life is not a matter of being shunted on to some track or abruptly off it Maybe reading is an act by which consciousness is changed in such an imperceptible manner that the reader is not even aware of it The reader suffering from amnesia in litteris is most definitely changed by his reading but without noticing it necause as he reads those critical faculties of his brain that could tell him that change is occurring are changing as well And for one who is himself a writer the sickness may conceivably be a blessing indeed a necessary precondition since it protects him against that crippling awe which every great work of literature creates and because it allows him to sustain a wholly uncomplicated relationship to plagiarism without which nothing original can be created Three Stories and a Reflection Patrick Suskind 1995 pp 82 86 This chapter describes additional CREOLE resources which do not form part of ANNIE 114 iw f MEM PATO Seto pe fil i fe MEA a os A VY LULE NAL RE SES iivl 8 1 Document Reset The document reset resource enables the document to be reset to its original state by remov ing all the annotation sets and their contents apart from the one containing the document format analysis Original Markups This resource is normally added to the beginn
134. ists of a comma separated list of blocks which are either anonymous or la belled We also allow the CPSL style shorthand notation as implemented in TextPro This is more limiting than code though e g I don t know how you could do the summing op eration below in CPSL Anonymous blocks will be evaluated within the same scope which encloses that of all named blocks and all blocks are evaluated in order so declarations can be made in anonymous blocks and then referenced in subsequent blocks Labelled blocks will only be evaluated when they were bound during LHS matching The symbol doc is always scoped to the Document which the Transducer this rule belongs to is processing For example match a sequence of integers and store their sum Rule NumberSum Token kind otherNum numberList gt numberList the running total int theSum 0 loop round all the annotations the LHS consumed for int i 0 i lt numberListAnnots length i get the number string for this annot String numberString doc spanStrings numberListAnnots nth i parse the number string and add to running total try 1 theSum Integer parseInt numberString catch NumberFormatException e ignore badly formatted numbers for each number annot doc addAnnotation number ERE E ae Cee ae tae ae ee ae en 1UuU numberListAnnots getLeftmostStart numberListAnnots getRightmostEnd sum new Integer
135. ities for text annotation The anno tation can be done manually by the user or semi automatically by running some processing resources over the corpus and then correcting adding new annotations manually Depend ing on the information that needs to be annotated some ANNIE modules can be used or adapted to bootstrap the corpus annotation task Since manual annotation is a difficult and error prone task GATE tries to make it simple to use and yet keep it flexible To add a new annotation select the text with the mouse e g FASE TAPA a ee ee er A ee A A ae SSR RES Eee Gate 2 0alpha3 build 657 3 lolx File Options Tools Help cate f heces al E Gh Applications Text Annotations Annotation Sets es AR 2 Defaut annotations d il 2 Processing Resources Latest News Last updated 6th September 2001 A PhD place has become vacant F Du al a Data stores Original markups ce C Ea Prospective Students Current Students Staff Visitors a br Dept Contact Details Site Map Search m center University C D Wa The Department ot Computer Science Regent Court r a of Sheftield 1 4DP n ma UNITED KINGDOM Tet 44 0 114 22 21800 r 1 Fax 44 0 114 22 21810 Email dept dcs shef ac uk p img O MR The Department of Computer Science University of Sheffield 1999 r me This page is maintained by WebMaster ra C a z Annotations Editor Features Editor Creates a new annotation _
136. itself There are lots of other classes produced along the way by the compiler compiler tools ASCII_CharStream java JJTParseCpslState java Node java ParseCpsl java ParseCpslConstants java ParseCpslTokenManager java ParseCpslTreeConstants java ParseException java SimpleNode java TestJape java Token java TokenMgrError java These live in the parser subpackage in the gate jape parser directory PE PRY E SIS Cee are E ae en S 1U1 Each grammar results in an object of class Transducer which has a set of Rule Constants are held in the interface JapeConstants The test harness is in TestJape B 6 Implementation B 6 1 A Walk Through The pattern application algorithm which is either like Doug s or like Brill s makes a top level call to something like boolean matches int position Document doc MutableInteger newPosition throws Postion0ut0fRange which is a method on each Rule This is in turn deferred to the rule s LeftHandSide and thence to the ConstraintGroup which each LeftHandSide contains The ConstraintGroup iterates over its set of PatternElementConjunctions when one succeeds the matches call returns true if none succeed it returns false The Rules also have void transduce Document doc throws LhsNotMatched methods which may be called after a successful match and result in the application of the RightHandSide of the Rule to the document PatternElements also implement the matches method Whenever it succeeds
137. java util import gate import gate creole import gate util Je This class is the implementation of the resource GOLDFISH http jakarta apache org ant a ea ee ee a a ee a cl public class GoldFish extends AbstractProcessingResource implements ProcessingResource class GoldFish The default XML configuration for GoldFish looks like this lt resource xml GoldFish gt lt hamish 26 9 2001 gt lt Id howto tex v 1 68 2002 07 18 15 25 49 hamish Exp gt lt CREOLE DIRECTORY gt lt CREOLE gt lt RESOURCE gt lt NAME gt GoldFish lt NAME gt lt JAR gt GoldFish jar lt JAR gt lt CLASS gt sheffield creole example GoldFish lt CLASS gt lt RESOURCE gt lt CREOLE gt lt CREOLE DIRECTORY gt The directory structure containing these files is shown in figure 2 3 GoldFish java lives GoldFish J build J classes J doc 3 lib 88 _J sheffield creole J example LU resources Figure 2 3 BootStrap directory tree in the src sheffield creole example directory creole xml is generated in the build SS SE O TAREA UNES AE A eRe SA ee directory from a source file called resource xml which lives in the src directory The lib directory is for libraries the classes directory is where Java class files are placed the doc directory is for documentation This process has the advantage that it creates a complete source tree and build structure for the compon
138. k jar If not set the scripts assume that these files live in the same directory as the script or in build and 1ib ext so that you can use the script for full source distributions of the system as well as the binary distributions Note the ZeroG installer sets this variable for you in the startup script GATE_CONFIG should point to the directory containing the site wide gate xml configu ration file if such is required see section 2 5 If this is not set the scripts check the directory found for item 2 for any gate xml file that may be present there The value of GATE_CONFIG is passed to the system by the scripts using either a i command line option or the Java property gate config 2 3 Troubleshooting On Windoze 95 and 98 you may need to increase the amount of environment space available for the gate bat script Right click on the script hit the memory tab and increase the initial environment value to maximum When our FTP server is overloaded you may get a blank download link in the email sent to you after you register Please try again later TAURO APO E ENEE AA ae Ae IS i ae 2 4 D Get Started with the GUI Probably the best way to learn how to use the GATE graphical development environment is to look at the animated demonstrations and tutorials on the movies page This section gives a short description of what is where in the main window of the system Figure 2 1 shows the main window of th
139. le below The first rule below shows a rule which matches a first person name e g Fred and adds a gender feature depending on the value of the minorType from the gazetteer list in which the name was found We first get the bindings associated with the person label i e the Lookup annotation We then create a new annotation called personAnn which contains this annotation and create a new FeatureMap to enable us to add features Then we get the minorType features and its value from the personAnn annotation in this case the feature SS SE O TAREA E UNES EE AE eee ee ee SS ee will be gender and the value will be male and add this value to a new feature called gender We create another feature rule with value FirstName Finally we add all the features to a new annotation FirstPerson which attaches to the same nodes as the original person binding Note that inputAS and outputAS represent the input and output annotation set Normally these would be the same by default when using ANNIE these will be the Default anno tation set Since the user is at liberty to change the input and output annotation sets in the paramters of the JAPE transducer at runtime it cannot be guaranteed that the input and output annotation sets will be the same and therefore we must specify the annotation set we are referring to Rule FirstName Lookup majorType person_first person gt
140. lities in diverse applications 3 by providing a development environment built on top of the framework made up of convenient graphical tools for developing components The architecture exploits component based software development object orientation and mobile code The framework and development environment are written in Java and These were at least our ideals of course we didn t completely live up to them 2 iw t MEM PATO Seto pe fil i a MMA Rachie scm os n dS VY LULE NAL RE SES Vv available as open source free software under the GNU library licence GATE uses Unicode Unicode Consortium 96 throughout and has been tested on a variety of Slavic Germanic Romance and Indic languages Maynard et al 01 Gamb ck amp Olsson 00 McEnery et al 00 From a scientific point of view GATE s contribution is to quantitative measurement of ac curacy and repeatability of results for verification purposes GATE has been in development at the University of Sheffield since 1995 and has been used in a wide variety of research and development projects Maynard et al 00 Ver sion 1 of GATE was released in 1996 was licensed by several hundred organisations and used in a wide range of language analysis contexts including Information Extrac tion Cunningham 99b Appelt 99 Gaizauskas amp Wilks 98 Cowie amp Lehnert 96 in En glish Greek Spanish Swedish German Italian and French Version 2 of the sys tem a complete r
141. ly based on the value of the parameter i e on the type of the parameter The name must be identical to the property of the resource that the parameter relates to DEFAULT default value RUNTIME doesn t need setting at initialisation time but must be set before calling execute Only meaningfull for PRs OPTIONAL not required COMMENT for display purposes Visual Resources also have a GUI tag which describes the resource PR or LR that it displays whether it is the main viewer for that resource or not main viewers are the first tab displayed for a resource and whether the VR should go in the small viewers window or the large one For example lt RESOURCE gt lt NAME gt Features Editor lt NAME gt lt CLASS gt gate gui FeaturesEditor lt CLASS gt lt type values can be large or small gt lt GUI TYPE large gt lt MAIN_VIEWER gt lt RESOURCE_DISPLAYED gt gate util FeatureBearer lt RESOURCE_DISPLAYED gt lt GUI gt lt RESOURCE gt More information SS SE O OO E UNES EE AE ee eee ee SS ee e To collect PRs into an application and run them see section 2 11 e GATE s internal creole xml file note that there are no JAR entries there as the file is bundled with GATE itself 2 11 D Create and Run an Application Once all the resources have been loaded an application can be created and run Right click on Applications and select New and then either Corpus Pipeline or Pi
142. mbol what is the set of reachable states The problem is that our input symbols are actually sets of restrictions This is similar to an automaton that has an infinite set of input symbols although any given set of rules describes a finite set of constraints This is not so bad the real problem is that we have to check if there are transitions that have the same restrictions We can safely consider that there are no two transitions with the same set of restrictions This is safe because if this assumption is wrong the result will be a state that has two transitions starting from it transitions that consume the same symbol This is not a problem because we have to check all outgoing transitions anyway we will only check the same transition twice This leads to the next issue Imagine the next part of the transition graph of a FSM Fig B 3 N IA Sets of restrictions A Common part Figure B 3 Example of transitions The restrictions associated to a transition are depicted as graphical figures the two coloured squares Now imagine that the two sets of restrictions have a common part the yellow triangle Let us assume that at one moment the current node in the FSM graph for one of the active PE RA a ae serene TON E Oe pra ue ee 110 FSM instances is state 1 We get from the annotation graph the set of annotations starting from the associated current node in the annotation graph and try to advance in the FSM transition graph In ord
143. me From now on she can follow this routine 1 Run her application on the email test corpus 2 Check the performance of the system by running the annotation diff tool to compare her manual results with the system s results This gives her both percentage accuracy figures and a graphical display of the differences between the machine and human outputs 3 Make edits to the code pattern grammars or gazetteer lists in her resources and recompile where necessary Perhaps because Outlook Express trashed her mail folder again or because she got tired of Microsoft specific viruses and hadn t heard of Netscape or Emacs 8While developing she uses a file URL for deployment she can put them on a web server iw t MEM PATO Seto pe fil i a MMA Rachie scm os n dS VY LULE NAL RE SES uz 4 Tell GATE to re initialise the resources 5 Goto 1 To make the alterations that she requires Fatima re implements the ANNIE gazetteer so that 1t regenerates itself from the local personnel data She then alters the pattern grammar in the semantic tagger to prioritise recognition of names from that source This latter job involves learning the JAPE language see chapter 6 but as this is based on regular expressions it isn t too difficult Eventually the system is running nicely and her accuracy is 93 there are still some problem cases e g when people use nicknames but the performance is good enough for production use Now Fat
144. me exception in a Prot g application outside GATE so this is not an integration specific problem It is best to give the full file name on creation or load of a Prot g project or on import of data files Unable to find Prot g plugin directory To gain some Prot g extra functionality you should have a Prot g plugin direc tory somewhere and to give the location of this directory to your Prot g applica tion You can find the Prot g plugin directory as a subdirectory of your Prot g installation http protege stanford edu download html named plugins You have to give full path to the Prot g installation You should add Java VM property Dprotege dir D projects Protege where D projects Protege is a directory with subdirectory plugins in it Another way is to copy this plugins directory in your GATE application directory Save Prot g project error when this project is not shown in the GATE tab pane There is a Prot g exception in this case The Prot g action for saving the project requires an active GUI So the simple solution is to doubleclick on resource to show the Prot g GUI in GATE Then you can use the Prot g specific popup menu actions without problem Chapter 5 Corpora Documents and Annotations Sometimes in life you ve got to dance like nobody s watching I think they should introduce sleeping to the Olympics It would be an excellent fi
145. mselves avoid directly constructing classes from other packages with a few exceptions such as JAPE s need for unattached annotation sets Instead they use the factory A 2 Exception Handling When and how to use exceptions Borrowing from Bill Venners here are some guidelines with examples 1 Exceptions exist to refer problem conditions up the call stack to a level at which they may be dealt with If your method encounters an abnormal condition that it can t handle it should throw an exception If the method can handle the problem ratio nally it should catch the exception and deal with it Example If the creation of a resource such as a document requires a URL as a parameter the method that does the creation needs to construct the URL and read from it If there is an exception during this process the GATE method should abort by throwing its own exception The exception will be dealt with higher up the food chain e g by asking the user to input another URL or by aborting a batch script do See ee LLZ 2 All GATE exceptions should inherit from gate util GateException a descendant of java lang Exception hence a checked exception or gate util GateRuntimeException a descendant of java lang RuntimeException hence an unchecked exception This rule means that clients of GATE code can catch all sorts of exceptions thrown by the system with only two catch statements This rule may be broken by methods that are not public s
146. n 20http gate ac uk gate doc papers html SS SE O OO TUNES EE Se eee ee eRe SA ee Bontcheva et al 02a ACI 2002 Workshop describes how GATE can be used as an en vironment for teaching NLP with examples of and ideas for future student projects developed within GATE Pastra et al 02 LREC 2002 discusses the feasibility of grammar reuse in applications using ANNIE modules Baker et al 02 LREC 2002 report results from the EMILLE Indic languages corpus collection and processing project Saggion et al 02b and Saggion et al 02a LREC 2002 SPLPT 2002 describes how ANNIE modules have been adapted to extract information for indexing multimedia material Maynard et al 01 RANLP 2001 discusses a project using ANNIE for named entity recognition across wide varieties of text type and genre Cunningham 00 PhD thesis defines the field of Software Architecture for Language Engineering reviews previous work in the area presents a requirements analysis for such systems which was used as the basis for designing GATE version 2 and evaluates the strengths and weaknesses of GATE version 1 Cunningham 02 Computers and the Humanities describes the philosophy and moti vation behind the system describes GATE version 1 and how well it lived up to its design brief McEnery et al 00 Vivek presents the EMILLE project in the context of which GATE s Unicode support for Indic languages has been developed Cunningh
147. n group chunks Two sets of tags are being used for experimentation shown in Table 8 1 Set I is very simple while Set II is richer because it encodes information about the head of the non recursive noun group this has a clear impact on system performance In addition to the chunk tags the corpus preserves the information about POS tags the Brown Corpus Tag Set The rules for text chunking take into consideration word identity and POS information as features for learning The learning procedure is as follows e baseline system from the corpus the number of times a given chunk tag occurs with LN AEREO ia e nd e Saal A AO EL a is a ee VY ELULE NAL RE SES iivl Tag Set I Tag and Definition Begin I Inside Chunk O Outside Chunk Tag and Definition Begin I Inside Chunk Dr B Talcott E led O a B team E of O researchers U E End Chunk from O U One Token Chunk Chunks Dr Talcott a team researchers O Outside Chunk Table 8 1 Two Tag Sets Used for Transformation based Chunking a given POS tag is computed Given a word with POS tag pos the baseline system assigns to the word the most probable chunk tag for pos as observed in the training corpus learning rules rules are learnt that correct the errors made by the baseline system The rules are learnt gradually for each position in the corpus where an error is observed in the baseline system These rules have the form CHANGE lt CHU
148. n is already tagged as a Person but could also be tagged as a date e g 16th Jan C 7 date jape This grammar contains the base rules for recognising times and dates Given the complexity of potential patterns representing such expressions there are a large number of rules and macros Although times and dates can be mutually ambiguous we try to distinguish between them as early as possible Dates times and years are generally tagged separately as TempDate TempTime and TempYear respectively and then recombined to form a final Date annota tion in a later phase This is because dates times and years can be combined together in many different ways and also because there can be much ambiguity between the three For example 1312 could be a time or a year while 9 10 could be a span of time or date or a fixed time or date C 8 reldate jape This grammar handles relative rather than absolute date and time sequences such as yes terday morning 2 hours ago the first 9 months of the financial year etc It uses mainly explicit key words such as ago and items from the gazetteer lists C 9 number jape This grammar covers rules concerning money and percentages The rules are fairly straight forward using keywords from the gazetteer lists and there is little ambiguity here except for example where Pound can be money or weight or where there is no explicit currency denominator ci ge VINA NC a Re aero a N
149. nal annotation type C 14 final jape This grammar uses the temporary annotations previously assigned in the earlier phases and converts them into final annotations The reason for this is that we need to be able to resolve ambiguities between different entity types so we need to have all the different entity types handled in a single grammar somewhere Ambiguities can be resolved using prioritisation ci VIM ARC a Re Aeron ere EE ae eS PR a et ivVi techniques Also we may need to combine previously annotated elements such as dates and times into a single entity The rules in this grammar use Java code on the RHS to remove the existing temporary annotations and replace them with new annotations This is because we want to retain the features associated with the temporary annotations For example we might need to keep track of whether a person is male or female or whether a location is a city or country It also enables us to keep track of which rules have been used for debugging purposes For the sake of obfuscation although this phase is called final it is not the final phase C 15 unknown jape This short grammar finds proper nouns not previously recognised and gives them an Un known annotation This is then used by the namematcher if an Unknown annotation can be matched with a previously categorised entity its annotation is changed to that of the matched entity Any remaining Unknown annotations are useful for debugging p
150. nal application and it is the same application embedded with some restrictions for example there is no menu or toolbar You have some of the Prot g menu items in GATE resource pop up menu 4 2 1 Opening Prot g projects and creating new ones To open a Prot g project you have to create a new GATE LR Language Resource Prot g Project In the field projectName put the file name of Prot g project it is best to give the full file name The second parameter is the URL for the Ontotext format ontology file You can use this parameter to save a Prot g ontology in Ontotext Ontology Editor format If you want to create a new project you have to leave the first parameter empty In this case you will be asked for Prot g project data format during creation The standard two are Standard Text Files and JDBC Database In the current installation of Prot g 2000 ver 1 7 you have a standard plugin included in the installation for RDF Schema format After creation of a Prot g Project LR you can open it by doubleclicking on resource in the GATE resource tree You will see the Prot g GUI inside the GATE tabbed pane 4 2 2 How to Import RDF files in Prot g project Whenever you load a Prot g project or create an empty one you can import another project using the popup menu item Import You will be asked if you want to save changes in the current project Select the format of the project you have
151. ndependently of the algorithms that process it For example a treebank can be developed independently of the parsers that may later be trained from it a thesaurus can be developed independently of the query expansion or sense tagging mechanisms that may later come to use it This type of data has come to have its own term Language Resources LRs LREC 1 98 covering many data sources from lexicons to corpora In recognition of this distinction we will adopt the following terminology Language Resource LR refers to data only resources such as lexicons corpora the sauri or ontologies Some LRs come with software e g Wordnet has both a user query interface and C and Prolog APIs but where this is only a means of accessing the underlying data we will still define such resources as LRs Processing Resource PR refers to resources whose character is principally program matic or algorithmic such as lemmatisers generators translators parsers or speech recognisers For example a part of speech tagger is best characterised by reference to the process it performs on text PRs typically include LRs e g a tagger often has a lexicon a word sense disambiguator uses a dictionary or thesaurus Additional terminology worthy of note in this context language data refers to LRs which are at their core examples of language in practice or performance data e g corpora of texts or speech recordings possibly including added descriptive i
152. nformation Extraction process running over documents 1 preserving the original document format with optional added annotations 2 in GATE s own XML serialisation format including all the annotations on the docu ment 3 by writing your own dump algorithm as a ProcessingResource This section describes how to use the first two options Both types of data export are available in the popup menu triggered by right clicking on a document in the resources tree see section 2 4 type 1 is called save preserving format and type 2 is called save as XML Selecting the save as XML option leads to a file open dialogue give the name of the file you want to create and the whole document and all its data will be exported to that file If you later create a document from that file the state will be restored Note because GATE s annotation model is richer than that of XML and because our XML dump implementation sometimes cuts corners the state may not be identical after restoration If your intention is to store the state for later use use a DataStore instead The save preserving format option also leads to a file dialogue give a name and the data you require will be dumped into the file The difference is that the file will preserve the original format of the source document You can add annotations to the dump file if there is a document viewer open in the main resource viewer area see section 2 4 then any annotations t
153. nformation as markup data about language refers to LRs which are purely descriptive such as a grammar or lexicon PRs can be viewed as algorithms that map between different types of LR and which typically use LRs in the mapping process An MT engine for example maps a monolingual corpus into a multilingual aligned corpus using lexicons grammars etc SOlder development methods like Jackson Structured Design Jackson 75 or Structured Analysis Yourdon 89 kept them largely separate 4A corpus of texts annotated with syntactic analyses gt This point is due to Wim Peters iw f MEM PATO Seto pe fil i fe MEA a os A VY ELULE NAL RE SES we Further support for the PR LR terminology may be gleaned from the argument in favour of declarative data structures for grammars knowledge bases etc This argument was current in the late 1980s and early 1990s Gazdar amp Mellish 89 partly as a response to what has been seen as the overly procedural nature of previous techniques such as augmented transition networks Declarative structures represent a separation between data about language and the algorithms that use the data to perform language processing tasks a similar separation to that used in GATE Adopting the PR LR distinction is a matter of conforming to established domain practice and terminology It does not imply that we cannot model the domain or build software to support it in an Object Oriented manner indeed the models in GATE are
154. nguage for input These options are all stored in the user s gate xml file When using GATE from the framework you can also set the site config location using Gate setSiteConfigFile File prior to calling Gate init When using GATE from the framework you can also set the site config location using Gate setSiteConfigFile File prior to calling Gate init 2 5 1 F Save Config Data to gate xml Arbitrary feature value data items can be saved to the user s gate xml file via the following API calls To get the config data Map configData Gate getUserConfig To add config data simply put pairs into the map configData put my new config key value To write the config data back to the XML file Gate writeUserConfig Note that new config data will simply override old values where the keys are the same In this way defaults can be set up by putting their values in the main gate xml file or the site gate xml file they can then be overridden by the user s gate xml file 2 6 Build GATE Note that you don t need to build GATE unless you re doing development on the system TAREA O ARE Se RO AE ee ee ee SS IT itself Prerequisites e A conforming Java environment as above e The UNIX shell tools make etc On Windoze use the excellent Cygwin distribution from Cygnus Red Hat available free from Cygwin com The UK mirrors facility HTTP The UK mirrors facility FTP If you re running
155. ningham EMILLE Building a Corpus of South Asian Languages Vivek A Quarterly in Artificial Intelligence 13 3 23 32 2000 Pastra et al 02 K Pastra D Maynard H Cunningham O Hamza and Y Wilks How feasible is the reuse of grammars for named entity recognition In Proceedings of 3rd Language Resources and Evaluation Conference 2002 Peters et al 98 W Peters H Cunningham C McCauley K Bontcheva and Y Wilks Uni form Language Resource Access and Distribution In Workshop on Distributing and Accessing Lexical Resources at Conference on Language Resources Evaluation Granada Spain 1998 Saggion et al 02a H Saggion H Cunningham K Bontcheva D Maynard C Ursu O Hamza and Y Wilks Access to Multimedia Information through Multisource and Multilanguage Information Extraction In 7th Workshop on Applications of Natural Language to Information Systems NLDB 2002 Stockholm Sweden 2002 Saggion et al 02b H Saggion H Cunningham D Maynard K Bontcheva O Hamza C Ursu and Y Wilks Extracting information for information indexing of multimedia material In Proceedings of 8rd Language Resources and Evaluation Conference LREC 2002 2002 Shaw amp Garlan 96 M Shaw and D Garlan Software Architecture Prentice Hall New York 1996 Stevenson et al 98 M Stevenson H Cunningham and Y Wilks Sense tagging and language engi neering In Proceedings of the 18th European Conference on Arti
156. nnotation Set Transfer a oe Sb SYS be Se Bee eS ee 119 9 Performance Evaluation of Language Analysers 120 9 1 The Annotation itt Tool 2 2 e areca a a God er a oe Ge See 120 9 2 The six annotation relations explained 0 o 121 9 3 Benchmarking tool a laa e 8S ee eS e a 122 9 4 Metrics for Evaluation in Information Extraction 123 10 Users Groups and LR Access Rights 125 10 1 Java serialisation and LR access rights o o 126 10 2 Oracle Datastore and LR access rights o o e e 0 126 10 2 1 Users Groups Sessions and Access Modes 126 10 2 2 User Group Administration bs poeta da daa 127 10 23 The APT menen e a a 130 Appendices 134 A Design Notes 134 Pol Patterns e sean can Gade Sa eh s ak tk Bad be he Pa Be ew eae we 134 ATL Components iaa e ae Be a A O A 135 A 1 2 Model view controller ra rs A ee a 136 a copa wiih Bags week we ee ee a Be oe ee Bee 137 A 2 Exception Handling 6 2 doe a RO SEE Oe e Bed 137 B JAPE Implementation 140 B 1 Formal Description of the JAPE Grammar 141 B 2 Relation to CPSE a or ba doe eh ets Gt wee Bee ce Be a BS 143 B 3 Algorithms for JAPE Rule Application 144 B 3 1 The first algorithm 2 4 6s Os A a ee hdr Bae arabe aoe 455 144 Boe SIROTA hoarse eG cards a ered a Ge Oh ser Seed Tag leak Mag ear ae ek 148 B 4 Label Binding Scheme ic bey goa eb ed ee a eS a 1
157. nt in applications Some working definitions 1 Computational Linguistics CL science of language that uses computation as an investigative tool 2 Natural Language Processing NLP science of computation whose subject mat ter is data structures and algorithms for computer processing of human language 3 Language Engineering LE building NLP systems whose cost and outputs are measurable and predictable 4 Software Architecture macro level organisational principles for families of systems In this context is also used as infrastructure 3http gate ac uk sale thesis iw f MEM PATO Seto pe fil i fe MEA Rachie scm os n dS VY ELULE NAL RE SES o 5 Software Architecture for Language Engineering SALE software infrastruc ture architecture and development tools for applied CL NLP and LE Of course the practice of these fields is broader and more complex than these definitions In the scientific endeavours of NLP and CL GATE s role is to support experimentation In this context GATE s significant features include support for automated measurement see section 9 providing a level playing field where results can easily be repeated across different sites and environments and reducing research overheads in various ways 1 3 Overview 1 3 1 Developing and Deploying Language Processing Facilities GATE as an architecture suggests that the elements of software systems that process natural language can usefully
158. nt candidate phrase 8 4 1 Prerequisites The phrases to be recognised should be listed in a set of files one for each type of occurrence as for the standard gazetteer The gazetteer is built with the information from a file that contains the set of lists which are files as well and the associated type for each list The file defining the set of lists should have the following syntax each list definition should be written on its own line and should iw t MEM PATO Seto pe fil i Se MME RS A PRA VY ELULE NAL RE SES iu contain e the file name required e the major type required e the minor type optional e the language s optional The elements of each definition are separated by The following is an example of a valid definition personmale l1st person male english Each file named in the lists definition file is just a list containing one entry per line When this gazetter is run over some input text a Gate document it will generate annotations of type Lookup having the attributes specified in the definition file 8 4 2 Setup In order to use this gazetteer from within GATE the following should reside in the creole setup file creole xml lt RESOURCE gt lt NAME gt OntoText Gazetteer lt NAME gt lt CLASS gt com ontotext gate gazetteer NaturalGazetteer lt CLASS gt lt COMMENT gt A list lookup component for documentation please refer to www ontotext com gate gazetteer documenta
159. nt the gate ProcessingResource interface e The last field selects the directory that you want the new resource created in For our example z tmp SS SE O TAE UNES EE AE ee eee Re SS ee Now we need to compile the class and create the JAR and XML files that allow GATE to load the new resource There s no reason not to use your own favourite alternative e g ANT For the pre requisites of the build process that we use based on Makefiles and the GNU shell tools see section 2 6 When you have these pre requisites available do the following from a command prompt working from the GoldFish build directory that the bootstrapper created for you configure make depend make make jar This will create the two files that GATE needs to load your new resource GoldFish jar and creole xml You can now load this resource into GATE see e section 2 8 for how to instantiate the resource from the framework e section 2 9 for how to load the resource in the development environment e section 2 10 for how to configure and further develop your resource which will by default do nothing The default Java code that was created for our GoldFish resource looks like this GoldFish java You should probably put a copyright notice here Why not use the GNU licence See http www gnu org hamish 26 9 2001 Id howto tex v 1 68 2002 07 18 15 25 49 hamish Exp package sheffield creole example import
160. o be dis tributed over the Web and uses XML for configuration of resources and GATE itself Resource implementations are stored at a URL when the resources are in the local file system this can be a file URL When the URL is given to GATE the creole xml component configuration file is sucked down the pipe and the resource information added to the CREOLE register When a user requests an instantiation of a resource the class files are sucked up too and an object created in the local virtual machine Language resource data can be stored in binary serialised form in the local file system or in an RDBMS like Oracle In the latter case communication with the database is over JDBC The Java DataBase Connectivity layer TAREA O AEREO Se SO Se eee ee eRe SA ee allowing the data to be located anywhere on the network or anywhere you can get Oracle running that is 3 2 Java Beans a Simple Component Architecture All GATE resources are Java Beans the Java platform s model of software components Beans are simply Java classes that obey certain interface conventions These conventions allow development tools such as GATE or Borland JBuilder to manipulate software com ponents without knowing very much about them The advantage of this is that users of such systems can extend them in diverse ways without having to touch the underlying core of the development tools The key parts of the Java Beans specification as used in GATE are
161. o long as their callers catch the non GATE exceptions and deal with them or convert them to GateException GateRuntimeException Almost all excep tions thrown by GATE should be checked exceptions the point of an exception is that clients of your code get to know about it so use a checked exception to make the compiler force them to deal with it Except Example With reference to the previous example a problem using the URL will be signalled by something like an UnknownHostException or an IOException These should be caught and re thrown as descendants of GateException 3 In a situation where an exceptional condition is an indication of a bug in the GATE library or in the implementation of some other library then it is permissible to throw an unchecked exception Example If a method is creating annotations on a document and before creating the annotations it checks that their start and end points are valid ranges in relation to the content of the document i e they fall within the offset space of the document and the end is after the start then if the method receives an InvalidOffsetException from the AnnotationSet add call something is seriously wrong In such cases it may be best to throw a GateRuntimeException 4 Where you are inheriting from a non GATE class and therefore have the exception signatures fixed for you you may add a new exception deriving from a non GATE class Example The SAX XML parser API uses SaxExcepti
162. o with creating resources We could have had ResourceUrlProblem and ResourceParameterProblem but that would probably have ended up with too many On the other hand just throwing everything as GateException is too coarse Hamish take note 7 Put exceptions in the package that they re thrown from unless they re used in many packages in which case they can go in gate util This makes it easier to find them in the documentation and prevents name clashes Example gate jape ParserException is correctly placed if it was in gate util it might clash with for example gate xml ParserException if there was such Appendix B JAPE Implementation The annual Diagram prize for the oddest book title of the year has been awarded to Gerard Forlin s Butterworths Corporate Manslaughter Service a hefty law tome providing guidance and analysis on corporate liability for deaths in the workplace The book not published until January was up against five other shortlisted titles Fancy Coffins to Make Yourself The Flat Footed Flies of Europe Lightweight Sandwich Construction Tea Bag Folding and The Art and Craft of Pounding Flowers No Paint No Ink Just a Hammer The shortlist was thrown open to readers of the literary trade magazine The Bookseller who chose the winner by voting on the magazine s website Butterworths Corporate Manslaughter Service a snip at 375 emerged as the overall victor with 35 The Diagram prize has been a regular
163. ocumentFormat gate Document aGateDocument URL url static public DocumentFormat getDocumentFormat gate Document aGateDocument String fileSuffix static public DocumentFormat getDocumentFormat gate Document aGateDocument MimeType mimeType The first two methods try to detect the right MimeType for the GATE document and after that they call the third one to return the reader associate with a MimeType GATE uses the implementation from http jigsaw w3 org for mime types The magic numbers test is performed using the information form magic2mimeTypeMap map Each key from this map is searched in the first bufferSize the default value is 2048 chars of text The method that does this is called runMagicNumbers InputStreamReader aReader and it belongs to DocumentFormat class More details about it can be found in the GATE API documentation In order to activate a reader to perform the unpacking the creole definition of a GATE document defines a parameter called markupAware initialized with a default value of true This parameter forces GATE to detect a proper reader for the document being read If no reader is found the document s content is load and presented to the user just like any other text editor this for textual documents SS SE O OO E UNES EE AE A ee SS ee The next subsections investigates particularities for each format and will describe the file extensions registered with each document format 5 5
164. odification of the members of the node Right pane In the right pane a single gazetteer list is displayed It can be edited and parts of it can be cut copied pasted 4 1 2 Ontology Editor This is a VR for editing the class hierarchy of an ontology it provides storing to and loading from RDF RDES and provides load edit store of the class hierarchy of an ontology Left pane The various ontologies loaded are lsited here On double click or right click and edit from the menu the ontology is visualized in the Right pane Right pane Besides the visualization of the class hierarchy of the ontology the following operations are allowed e expanding collapsing parts of the ontology e adding a class in the hierarchy by right clicking on the intended parent of the new class and choosing add sub class e removing a class via right clicking on the class and choosing remove As a result of this VR the ontology definition file is affected altered aS SEE O OO E UNES Se A ee SS IT 4 2 Prot g in GATE Prot g is integrated in GATE so that people with developed Prot g ontologies can use them in GATE for example in the hierarchical gazetteer and also so that they can take advantage of being able to read different format ontology files in Prot g It is best to download and investigate Prot g http protege stanford edu index html be fore trying to use it from GATE In GATE you will have the same Prot g GUI as in the origi
165. ognition over these annotations by specifying this annotation set as the input annotation set for all the following resources In this example we would set the following parameters assuming that the annotations from the tokenise and gazetteer are initially placed in the Default annotation set e inputASName Default e outputASName Filtered e tagASName Original markups e textTagName BODY 2 10 D F Configure CREOLE Resources This section desscribes how to write entries in the creole xml file that is used to describe resources to GATE This data is used to tell GATE things like what parameters a resource has how to display it if it has a visualisation etc An example file lt CREOLE DIRECTORY gt lt CREOLE gt lt RESOURCE gt lt NAME gt GATE XML Document Format lt NAME gt lt CLASS gt gate corpora Xml1DocumentFormat lt CLASS gt lt AUTOINSTANCE gt lt PRIVATE gt lt JAR gt gate jar lt JAR gt lt RESOURCE gt lt CREOLE gt lt CREOLE DIRECTORY gt These files have as a root element CREOLE DIRECTORY and may contain any number of CREOLE elements which in turn contain any number of RESOURCE elements Each resource must give a name a Java class and the JAR file that it can be loaded from The above example defines GATE s XML document format analyser resource This resource 12The purpose of the CREOLE element is to allow files to be build up from the concatenation of multiple other files SS SE O O
166. on and is given the new annotation Person Rule PersonJobTitle Priority 20 Lookup majorType jobtitle jobtitle TempPerson person jobtitle JobTitle rule PersonJobTitle person Person kind personName rule PersonJobTitle TAE O OO E UNES ES Se eee ee Re SS ee Similarly labelled patterns can be nested as in the example below where the whole pattern is annnotated as Person but within the pattern the jobtitle is annotated as JobTitle Rule PersonJobTitle2 Priority 20 Lookup majorType jobtitle jobtitle TempPerson person gt jobtitle JobTitle rule PersonJobTitle person Person kind personName rule PersonJobTitle Grammar rules can essentially be of two types The first type of rule involves no gazetteer lookup but can be defined using a small set of possible formats In general these are fairly straightforward and offer little potential for ambiguity The second type of rules rely more heavily on the gazetteer lists and cover a much wider range of possibilities This not only means that many rules may be needed to describe all situations but that there is a much greater potential for ambiguity This leads to the necessity for rule ordering and prioritisation as will be discussed below For example a single rule is sufficient to identify an IP address because there is only one basic format a series of numbers each set connec
167. on Implementing a SAX parser for a document type involves overiding methods that throw this exception Where you want to have a subtype for some problem which is specific to GATE processing you could use GateSaxException which extends SaxException 5 Test code is different in the JUnit test cases it is fine just to declare that each method throws Exception and leave it at that The JUnit test runner will pick up the excep tions and report them to you Test methods should however try and ensure that the exceptions thrown are meaningful For example avoid null pointer exceptions in the test code itself e g by using assertNonNull a SS a rc indi ive Example public void testComments throws Exception ResourceData docRd ResourceData reg get gate Document assertNotNull testComments couldn t find document res data docRd String comment docRd getComment assert testComments incorrect or missing COMMENT on document comment null amp amp comment equals GATE document testComments See also the testing notes 6 Throw a different exception type for each abnormal condition You can go too far on this one a hundred exception types per package would certainly be too much but in general you should create a new exception type for each different sort of problem you encounter Example The gate creole package has a ResourcelnstantiationException this deals with all problems to d
168. on the award circuit since 1978 when Proceedings of the Second International Workshop on Nude Mice carried off the inaugural award Since then titles such as American Bottom Archaeology and last year s winner High Performance Stiffened Structures an engineering publication have received unwonted publicity through the prize This year s winner is perhaps most notable for its lack of entendre Manslaughter Service kills off competition in battle of strange titles Emma Yates The Guardian November 30 2001 This chapter gives implementation details and formal definitions of the JAPE annotation patterns language Section B 1 gives a more formal definition of the JAPE grammar and some examples of its use Section B 2 describes JAPE s relation to CPSL The next 3 sections describe the algorithms used label binding and the classes used Section B 6 gives an example of the implementation and finally section B 7 explains the compilation process 140 PE PRY A Ieee Cee are tee ae ee ae en S 14 11 B 1 Formal Description of the JAPE Grammar JAPE is similar to CPSL a Common Pattern Specification Language developed in the TIPSTER programme by Doug Appelt and others with a few exceptions Figure B 1 gives a BNF Backus Naur Format description of the grammar An example rule LHS Rule KiloAmount Token kind containsDigitAndComma number Token string kilograms whole A basic constraint specification appears betw
169. on tools use JAPE amongst other things For information on JAPE see e chapter 6 describes how to write JAPE rules e chapter 7 describes the built in IE components e appendix B describes how JAPE is implemented and formally defines the language s grammar e appendix C describes the default Named Entity rules distributed with GATE 2 21 F Embed NLE in other Applications Embedding GATE based language processing in other applications is straightforward e add gate jar to the CLASSPATH e g CLASSPATH home hamish gate bin gate jar e tell Java that the GATE Unicode Kit is an extension Djava ext dirs home hamish gate bin for example e initialise GATE with gate Gate init e program to the framework API For example this code will create the ANNIE extraction system public static void main String args throws GateException IOException initialise the GATE library Gate init initialise ANNIE create a corpus pipeline controller to run ANNIE with annieController SerialAnalyserController Factory createResource gate creole SerialAnalyserController Factory newFeatureMap Factory newFeatureMap ANNIE_ Gate genSym A ey So tO Se RO Se ee eee ee SS ee load each PR as defined in ANNIEConstants for int i 0 i lt ANNIEConstants PR_NAMES length i FeatureMap params Factory newFeatureMap use default parameters ProcessingResource pr ProcessingResource Facto
170. ontrol appelt debug true Avoid conflicts If two possible ways of matching are found for the same text string a conflict can arise Normally this is handled by the priority mechanism test length rule priority and finally rule precedence If all these are equal Jape will simply choose a match at random and fire it This leads ot non deterministic behaviour which should be avoided Using Java code on the RHS If you want to be flash you can use any Java code you like on the RHS of the rule This is useful for feature percolation see below for deleting previous annotations measuring length of strings and performing alternative operations depending on particular features of the annotation See 6 4 for more details Feature percolation To copy features from previous annotations where the value of the feature is unknown some simple Java code can be used See Section 6 4 for a more detailed explanation of this Adding a feature to the document Instead of adding a feature to an annotation a feature can be added to the document as a whole For example the following code on the RHS would add the feature texttype with value sport to the document doc getFeatures put texttype sport 6 4 Using Java code in JAPE rules The RHS of a JAPE rule can consist of any Java code This is useful for removing temporary annotations and for percolating and manipulating features from previous annotations In the examp
171. ormat lt NAME gt lt CLASS gt mypackage Xm1DocumentFormat lt CLASS gt lt AUTOINSTANCE gt lt PRIVATE gt lt RESOURCE gt More information on the operation of GATE s document format analysers may be found in section 5 5 TAREA O OO E TUNES ES AE AN ee SS ee 2 23 D F Create a New Annotation Schema GUI An annotation schema file can be loaded or unloaded in GATE just like any other language resource Once loaded into the system the SchemaAnnotationEditor will use this definition when creating or editing annotations API Another way to bring an annotation schema inside GATE is through creole xml file By using the AUTOINSTANCE element one can create instances of resources defined in creole xml The gate creole AnnotationSchema which is the Java representation of an annotation schema file initializes with some predefined annotation definitions annotation schemas as specified by the GATE team Example from GATE s creole xml lt Annotation schema gt lt RESOURCE gt lt NAME gt Annotation schema lt NAME gt lt CLASS gt gate creole AnnotationSchema lt CLASS gt lt COMMENT gt An annotation type and its features lt COMMENT gt lt PARAMETER NAME xmlFileUr1 COMMENT The url to the definition file SUFFIXES xml xsd gt java net URL lt PARAMETER gt lt AUTOINSTANCE gt lt PARAM NAME xml1FileUrl VALUE gate creole schema DateSchema xml gt lt AUTOINSTANCE gt lt AUTOINSTANCE gt lt PARAM N
172. ormed just like in the plain text case All annotation created have as type the name of the e mail s fields and they are placed in the Original markup annotation set Example From someoneQzzz zzz zzz Wed Sep 6 10 35 50 2000 Date Wed 6 Sep2000 10 35 49 0100 BST From forenamel surname2 lt someonel yyy yyy xxx gt To forename2 surname2 lt someone2 ddd dddd dd dd gt Subject A subject Message ID lt Pine SOL 3 91 1000906103251 26010A 100000 servername gt MIME Version 1 0 Content Type TEXT PLAIN charset US ASCII This text belongs to the e mail body This is a paragraph in the body of the e mail This is another paragraph GATE attempts to detect lines such From someone zzz z2z z2z2 Wed Sep 6 10 35 50 2000 in the e mail text Those lines separate e mail messages contained in one file After that for each field in the e mail message annotation are created as follows The annotation type will be the name of the field the feature map will be empty and the annotation will span from the end of the filed until the end of the line containing the e mail field TAREA O TAE E UNES E Se ve eee AE SS ITA Example al type date al spans between the two Date Wed 6Sep2000 10 35 49 0100 BST a a2 type from a2 spans between the two From forenamel surname2 lt someonel yyy yyy xxx gt 7 The extensions associate with the email reader are e eml e email e mail The web
173. orted according to their offset in the text For each pronoun in the list the following actions are performed If the pronoun is it then a check is performed if this is a pleonastic occurrence and if so then no further attempt for resolution is made The proper context is determined The context size is expressed in the number of sentences it will contain The context always includes the current sentence the one containing the pronoun the preceding sentence and zero or more preceding sentences Depending on the type of pronoun a set of candidate antecedents is proposed The candidate set includes the named entities that are compatible with this pronoun For example if the current pronoun is she then only the Person annotations with gender feature equal to female or unknown will be considered as candidates From all candidates one is chosen according to evaluation criteria specific for the pronoun Lv O ia co a e pe fil A fe MEA a os A VY LULE NAL RE SES UL Coreference chain generation This step is actually performed by the main module After executing each of the submodules on the current document the coreference module follows the steps e Retrieves the anaphor antecedent pairs generated from them e For each pair the orthographic matches if any of the antecedent entity is retrieved and then extended with the anaphor of the pair i e the pronoun The result is the coreference chain for the entity Th
174. ous facilities for working with Unicode beyond those that come as default with Java 1 a Unicode editor with input methods for many languages 2 use of the input methods in all places where text is edited in the GUI 3 a development kit for implementing input methods ability to read diverse character encodings 1 using the editor In the GUI select Unicode editor from the Tools menu This will display an editor window and when a language with a custom input method is selected for input see next section a virtual keyboard window with the characters of the language assigned to the keys on the keyboard You can enter data either by typing as normal or with mouse clicks on the virtual keyboard 2 configuring input methods In the editor and in GATE s main window the Options menu has an Input methods choice All supported input languages a superset of the JDK languages are available here Note that you need to use a font capable of displaying the language you select By default GATE will choose a Unicode font if it can find one on the platform you re running on Otherwise select a font manually from the Options menu Configuration choice 3 using the development kit GUK the GATE Unicode Kit is documented at http gate ac uk gate doc javadoc guk package 4 reading different character encodings When you create a document from a URL pointing to textual data in GATE you have to
175. paring annotations Typically a language analysis component developer will mark up some documents by hand and then use these along with the diff tool to automatically measure the performance of the components See section 9 e GUK the GATE Unicode Kit fills in some of the gaps in the JDK s support for Unicode e g by adding input methods for various languages from Urdu to Chinese See section 2 28 for more details And by version 3 it will make a mean cup of tea 6 JDK Java Development Kit Sun Microsystem s Java implementation Unicode support is being actively improved by Sun but at the time of writing many languages are still unsupported In fact Unicode itself doesn t support all languages e g Sylheti hopefully this will change in time iw t MEM PATO Seo e fil i a MMA a os A VY LULE NAL RE SES au 1 3 4 An Example This section gives a very brief example of a typical use of GATE to develop and deploy language processing capabilities in an application and to generate quantitative results for scientific publication Let s imagine that a developer called Fatima is building an email client for Cyberdyne Systems large corporate Intranet In this application she would like to have a language processing system that automatically spots the names of people in the corporation and transforms them into mailto hyperlinks A little investigation shows that GATE s existing components can be tailored to this purpose Fatima sta
176. pe GoldFish First start the GATE development environment see section 2 2 From the Tools menu select BootStrap Wizard which will pop up the dialogue in figure 2 2 The meaning of the data entry fields eo oe See AA A A eee ea oe oe E BootStrap Wizard x Resource name e g myMorph GoldFish Resource package e g sheffield creole morph shetfield creole example Resource type ProcessingResource v Implementing class name e g Morpher GoldFish Interfaces implemented pate ProcessingResource Create in folder z tmp Browse Finish Cancel Figure 2 2 BootStrap Wizard Dialogue e The resource name will be displayed when GATE loads the resource and will be the name of the directory the resource lives in For our example GoldFish e Resource package is the Java package that the class representing the resource will be created in For our example sheffield creole example e Resource type must be one of Language Processing or Visual Resource In this case we re going to process documents and add annotations to them so we select ProcessingResource e Implementing class name is the name of the Java class that represents the resource For our example GoldFish e The interfaces implemented field allows you to add other interfaces e g java util Set that you would like your new resource to implmenent In this case we just leave the default which is to impleme
177. peline A pipeline application can only be run over a single document while a corpus pipeline can be run over a whole corpus To build the pipeline double click on it and select the resources needed to run the applica tion you may not necessarily wish to use all those which have been loaded Transfer the necessary components from the set of loaded components displayed on the left hand side of the main window to the set of selected components on the right by selecting each com ponent and clicking on the left and right arrows or by double clicking on each component Ensure that the components selected are listed in the correct order for processing starting from the top If not select a component and move it up or down the list using the up down arrows at the left side of the pane Ensure that any parameters necessary are set for each processing resource by clicking on the resource from the list of selected resources and check ing the relevant paramters from the pane below For example if you wish to use annotation sets other than the Default one these must be defined for each processing resource Note that if a corpus pipeline is used the corpus needs only to be set once using the drop down menu beside the corpus box If a pipeline is used the document must be selected for each processing resource used Finally right click on Run to run the application on the document or corpus For how to use the conditional
178. pipeline Simple pipelines simply group a set of PRs together in order and execute them in turn The implementing class is called SerialController Corpus pipelines are specific for LanguageAnalysers PRs that are applied to documents and corpora A corpus pipeline opens each document in the corpus in turn sets that document as a runtime parameter on each PR runs all the PRs on the corpus then closes the document The implementing class is called SerialAnalyserController Conditional versions of these controllers are also available These allow processing resources to be run conditionally on document features See Section 2 12 for how to use these 3 7 Language Resources and Datastores Language Resources can be stored in Data Stores Data Stores are an abstract model of disk based persistence which can be implemented by various types of storage mechanism Currently two such mechanisms are implemented Serial Data Stores are based on Java s serialisation system and store data directly into files and directories TAREA O OO Se RO AE A ee SA ee Oracle Data Stores store data into an Oracle RDBMS For details of how to set up an Oracle DB for GATE see http gate ac uk gate doc persistence pdf PostgreSQL Data Stores store data into a PostgreSQL RDBMS For details of how to set up a PostgreSQL DB for GATE see http gate ac uk gate doc persistence pdf 3 8 Built in CREOLE Resources GATE comes with various built in components
179. pports documents in a variety of formats including XML RTF email HTML SGML and plain text In all cases the format is analysed and converted into a sin gle unified model of annotation The annotation format is a modified form the TIP STER format Grishman 97 which has been made largely compatible with the Atlas format Bird amp Liberman 99 and uses the now standard mechanism of stand off markup GATE documents corpora and annotations are stored in databases of various sorts visualised via the development environment and accessed at code level via the framework See chapter 5 for more details of corpora etc A family of Processing Resources for language analysis is included in the shape of ANNIE A Nearly New Information Extraction system These components use finite state techniques to implement various tasks from tokenisation to semantic tagging or verb phrase chunking All ANNIE components communicate exclusively via GATE s document and annotation resources See chapter 7 for more details See chapter 4 for visual resources See chapter 8 for other miscellaneous CREOLE resources 1 33 Additional Facilities Three other facilities in GATE deserve special mention e JAPE a Java Annotation Patterns Engine provides regular expression based pat tern action rules over annotations see chapter 6 e The annotation diff tool in the development environment implements performance metrics such as precision and recall for com
180. presented at the same level as a document and has nodes called calsses for con sistency with RDFs ad DAML OIL though they are really just types The OntoGazetteer assigns classes rather than major or minor types and is aware of mappings between lists and class IDs There are two Visual Resources one for editing the standrad gazetteer lists including the definition files and the mappings to the ontology and one for editing the ontology itself 60 iw t MEM PATO Seto pe fil i fe MMA ES e easels VY LULE NAL RE SES Vs 4 1 1 Gazetteer Lists Editor and Mapper This is a VR for editing the gazetteer lists and mapping them to classes in an ontology It provides load store edit for the lists load store edit for the mapping information loading of ontologies load store edit for the linear definition file and mapping of the lists file to the major type minor type and language Left pane A single ontology is visualized in the left pane of the VR The mapping between a list and a class is displayed by showing the list as a subclass with a different icon The mapping is specified by drag and drop from the linear defintion pane in the middle and or by right click menu Middle pane The middle pane displays the nodes lines in the linear defintion file By double clicking on a node the corresponding list is opened Editing of the line node is done by right clicking and choosing edit a dialogue appears lower part of the scheme allowing the m
181. raph will become max A endNode Az endNode 1By this we mean restrictions referring to the same type of annotations If for branches 1 2 and 1 3 the restrictions for the type T are the same the query for type T will be run only once Each of the two branches can also have restrictions for other types of annotations a iii itv While building the compound matcher it is possible to detect queries that depend one from another e g if the expected results of a query are a subset of the results from another query This kind of situations can be marked so when the queries are actually run some operations can be avoided e g if the less restrictive search returned no results than the more restrictive one can be skipped or if a search returns an AnnotationSet an object that can be queried than the more restrictive query can be B 3 2 Algorithm 2 Consider the following figure ol e DA Ee oi SS 7 10 del MIL x k 1 o e Node Annotation description Annotation type amp features Figure B 6 An annotation graph Basically the algorithm has to traverse this graph starting from the leftmost node to the rightmost one Each path found is a sequence of possible matches Because more than one annotation all starting at the same point can be matched at one step a path is not viewed as a classical path in a graph but a sequence of steps each step being a set of annotations that start in the same node e g
182. re supported by GATE e Plain Text e HTML e SGML e XML e RIF e Email By default GATE will try and identify the type of the document then strip and convert any markup into GATE s annotation format To disable this process set the markupAware parameter on the document to false When reading a document of one of these types GATE extracts the text between tags where such exist and create a GATE annotation filled as follows The name of the tag will constitute the annotation s type all the tags attributes will mate rialize in the annotation s features and the annotation will span over the text covered by the tag A few exceptions of this rule apply for the RTF Email and Plain Text formats which will be described later in the input section of these formats The text between tags is extracted and appended to the GATE document s content and all annotations created from tags will be placed into a GATE annotation set named Original markups TAREA O ARE Se RO AE ee ee ee SS IT Example If the markup is like this lt aTagName attribi valueil attrib2 value2 attrib3 value3 gt A piece of text lt aTagName gt then the annotation created by GATE will look like annotation type aTagName annotation fm attribi valuel atrtrib2 value2 attrib3 value3 annotation start startNode annotation end endNode The startNode and endNode are created from offsets refereing the beginning and the end of A piece
183. rea a black rectangle which is the small resource viewer 4 on the right of the main area containing tabs with Messages and GATE Docu ment_0001F the main resource viewer 5 the messages bar along the bottom where it says Finished dumping The menu and the messages bars do the usual things Longer messages are displayed in the messages tab in the main resource viewer area The resource tree and resource viewer areas work together to allow the system to display diverse resources in various ways Visual Resources integrated with GATE can have a small view or a large view For example data stores have a small view documents have a large view All the resources applications and datastores currently loaded in the system appear in the resources tree double clicking on a resource will load a viewer for the resource in one of the resource view areas 2 5 D F Configure GATE When the GATE development environment is started or when Gate init is called from the API GATE loads various sorts of configuration data stored as XML in files generally called something like gate xml or gate xml This data holds information such as e whether to save settings on exit e what fonts the GUI should use e where the local Oracle database lives All of this type of data is stored at three levels in order from general to specific e the system wide level located in the GATE resources packaged with the system e
184. required for the tagger The splitter uses a gazetteer list of abbreviations to help distinguish sentence marking full stops from other kinds Each sentence is annotated with the type Sentence Each sentence break such as a full stop is also given a Split annotation This has several possible types punctuation CR a line break or multi a series of punctuation marks such as The sentence splitter is domain and application independent 7 4 Part of Speech Tagger The tagger Hepple 00b is a modified version of the Brill tagger which produces a part of speech tag as an annotation on each word or symbol The tagger uses a default lexicon and ruleset the result of training on a large corpus taken from the Wall Street Journal Both of these can be modified manually if necessary Two additional lexicons exist one for texts in all uppercase lexicon_cap and one for texts in all lowercase lexicon lower To use these the default lexicon should be replaced with the appropriate lexicon at load time The default ruleset shoud sitll be used in this case 7 5 Semantic Tagger ANNIE s semantic tagger is based on the JAPE language see chapter refchap jape It contains rules which act on annotations assigned in earlier phases in order to produce outputs of annotated entities 7 6 Orthographic Coreference OrthoMatcher Note this component was previously known as a NameMatcher
185. riate java file s compile them and package them as a jar The GATE development environment provides a bootstrap tool to start this process see section 2 7 Alternatively you can simply copy code from an existing resource Instantiating a resource in the framework To create a resource in your own Java code use GATE s Factory class this takes care of parameterising the resource restor ing it from a database where appropriate etc etc Section 2 8 describes how to do this SS SE O OO UNES EE Se eee eee ee SS ee Loading a resource in the development environment To load a resource in the de velopment environment use the various New resource options from the File menu and elsewhere See section 2 9 Resource configuration and implementation GATE s bootstrap tool will create an empty resource that does nothing In order to achieve the behaviour you require you ll need to change the configuration of the resource by editing the creole xml file and or change the Java code that implements the resource See section 2 10 More details of the specifics of tasks related to these phases are available in chapter 2 3 6 Processing Resources and Applications PRs can be combined into applications Applications model a control strategy for the exe cution of PRs In the framework applications are called controllers accordingly Currently only sequential or pipeline execution is supported There are two types of
186. rom the RHS by gt The following operators can be used on the LHS or 0 or more occurrences 0 or 1 occurrences 1 or more occurrences od The RHS uses as a separator and has the following format LHS gt Annotation type attribute1 value1 attribute n value n Details about the primitive constructs available are given in the tokeniser file Default To keniser Rules The following tokeniser rule is for a word beginning with a single capital letter UPPERCASE_LETTER LOWERCASE_LETTER gt Token orth upperInitial kind word It states that the sequence must begin with an uppercase letter followed by zero or more lowercase letters This sequence will then be annotated as type Token The attribute orth orthography has the value upperlnitial the attribute kind has the value word 7 1 2 Token Types In the default set of rules the following kinds of Token and SpaceToken are possible A ey So tO Se RO Se ee eee ee SS ee iV Word A word is defined as any set of contiguous upper or lowercase letters including a hyphen but no other forms of punctuation A word also has the attribute orth for which four values are defined e upperInitial initial letter is uppercase rest are lowercase e allCaps all uppercase letters e lowerCase all lowercase letters e mixedCaps any mixture of upper and lowercase letters not included in the abo
187. rson iterator next gate FeatureMap features Factory newFeatureMap features put gender personAnn getFeatures get gender features put rule GazPersonFirst outputAS add person firstNode person lastNode TempPerson features inputAS removeAll person 6 4 1 Adding a feature to the document The following example code shows how to add the feature genre with value email to the document using JAVA code on the RHS of a rule Rule Email Priority 150 message gt doc getFeatures put genre email 6 5 Optimising for speed The way in which grammars are designed can have a huge impact on the processing speed Some simple tricks to keep the processing as fast as possible are e avoid the use of the and operators Replace them with where possible For example instead of Token use SS A a e ee OO Se a ee ee Re SS ee Token Token Token if you can predict that you won t need to recognise a string of Tokens longer than 3 e avoid specifying unnecessary elements such as SpaceTokens where you can To do this use the Input specification at the beginning of the grammar to stipulate the annotations that need to be considered If no Input specification is used all annotations will be considered so for example you cannot match two tokens separated by a space unless you specify the SpaceToken in the pattern If howe
188. rts up the development environment and creates a new document containing some example emails She then loads some processing resources that will do named entity recognition a tokeniser gazetteer and semantic tagger and creates an application to run these components on the document in sequence Having processed the emails she can see the results in one of several viewers for annotations The GATE components are a decent start but they need to be altered to deal specially with people from Cyberdyne s personnel database Therefore Fatima creates new cyber vesions of the gazetteer and semantic tagger resources using the bootstrap tool This tool creates a directory structure on disk that has some Java stub code a Makefile and an XML configuration file After several hours struggling with badly written documentation Fatima manages to compile the stubs and create a JAR file containing the new resources She tells GATE the URL of these files and the system then allows her to load them in the same way that she loaded the built in resources earlier on Fatima then creates a second copy of the email document and uses the annotation editing facilities to mark up the results that she would like to see her system producing She saves this and the version that she ran GATE on into her Oracle datastore set up for her by the Herculean efforts of the Cyberdyne technical support team who like GATE because it enables them to claim lots of overti
189. ry as for the creation of a transient resource but this time you tell it the data store the resource came from and the ID of the resource in that datastore URL u URL of a serial data store directory SerialDataStore sds new SerialDataStore u toString sds open getLrIds returns a list of LR Ids so we get the first one Object lrId sds getLrIds gate corpora DocumentImp1 get 0 we need to tell the factory about the LR s ID in the data store and about which data store it is in we do this via a feature map FeatureMap features Factory newFeatureMap features put DataStore LR_ID_FEATURE_NAME lrld features put DataStore DATASTORE_FEATURE_NAME sds read the document back Document doc Document Factory createResource gate corpora DocumentImpl features 2 9 D Load CREOLE Resources 2 9 1 Loading Language Resources Load a language resource by right clicking on Language Resources and selecting a language resource type document corpus or annotation schema Choose a name for the resource and choose any parameters as necessary For a document a file or url should be selected as the value of sourceUrl double clicking in the values box brings up a tree structure to enable selection of documents Other parameters can be selected or changed as necessary such as the encoding of the document and whether it should be markup aware SS SE O AREA E UNES E
190. ry createResource ANNIEConstants PR_NAMES i params add the PR to the pipeline controller annieController add pr for each ANNIE PR A longer example of embedding ANNIE is available at http gate ac uk GateExamples doc 2 22 D F Add support for a new document format In order to add a new document format one needs to extend the gate DocumentFormat class and to implement an abstract method called public void unpackMarkup Document doc throws DocumentFormatException This method is supposed to implement the functionality of each format reader and to create annotation on the document Finally the document s old content will be replaced with a new one containing only the text between markups see the GATE API documentation for more details on this method functionality If one needs to add a new textual reader will extend the gate corpora TextualDocument Format and override the unpackMarkup doc method This class needs to be implemented under the Java bean specifications because it will be instantiated by GATE using Factory createResource method The init method that one needs to add and implement is very important because in here the reader defines its means to be selected successfully by GATE What one need to do is to add some specific information into certain static maps defined in DocumentFormat class that will be used at reader detection time After that a definition of the reader will be placed into the
191. s 120 SS oi See ee ae ee rE Se ee il a L e The Lenient measure considers all partially correct responses as correct e The Average measure allocates a half weight to partially correct responses i e it takes the average of strict and lenient It can be accessed both from GUI or from the API Annotation Diff compares sets of an notations with the same type When performing the diff the annotation offsets and their features will be taken into consideration and after that the diff process is triggered Figure 9 1 shows a part of the AnnotationDiff viewer Features Select annot type Oa Locat ion v Some UK 2801 2803 UK 2801 2803 Swanwick 2628 2636 y UK 931 933 UK 931 933 Precision strict 1 0000 Recall strict 0 6667 F Measure strict 0 8000 Precision average 1 0000 Recall average 0 6667 F Measure average 0 8000 Precision lenient 1 0000 Recall lenient 0 6667 F Measure lenient 0 8000 Figure 9 1 Part of the AnnotationDiff viewer All annotations from the key set are compared with the ones from the response set and those found to have the same start and end offsets are displayed on the same line in the table Next Annotation Diff evaluates if the features of each annotation from the response set subsume those features from the key set as specified by the keyFeatureNamesSet parameter To understand this in more detail see section 2 18 which describes the Annotation Diff param
192. s 3 3 ase ww a A 1 3 3 Additional Facilities eke Gi hut ee es ee eo e tena E34 An Example y 5 caro Y dod A Sd Sak We wa a de ee ee SS 1 4 Structure of the Books alar dara ce ee a Ip cPurther Reading arsos oa oy A A eal Spe a See ea Speed How To 2 1 Download GATE lt a e Bees ee OS SE 22 Installand Run GATE La eds Bas A e d e dh geo a aa 2 2 1 The Basy Way sessi cona Seek ee See ok be eS oe Ss 2 2 2 The Hard Way 33 2o6 wind rl dm ae eed ease Wd oe BS 2 3 VroubleshO ting s cacr 4 40 ie ba Gee SR ee ee GO AEN ee eS 2 4 D Get Started withthe GUI 0 o o EA RR a EA E 2 5 1 F Save Config Data to gatexml o 26 Bud AE 3y bice e ake erst aja e pde he e a e aa 2 7 D F Create a New CREOLE Resource 0 e 2 8 F Instantiate CREOLE Resources o 0 e 2 9 D Load CREOLE Resources o 0 ee eee eee 2 9 1 Loading Language Resources o 2 9 2 Loading Processing Resources 0 2 10 D F Configure CREOLE Resources o o e 2 2 11 D Create and Run an Application o e e 2 12 D Run PRs Conditionally on Document Features 219 D View Annotalons dd A AA AAA 2 14 D Do Information Extraction with ANNIE o 2 15 D Create and Edit Test Data o voor esca ak ee a 2 15 1 Schema Annotation Editor
193. s control treaties He has been used to justify even President Bush s missile defence programme though neither he nor his associates are known to possess anything approaching ballistic missile technology Now he has become the personification of evil required to launch a crusade for good the face behind the faceless terror The closer you look the weaker the case against Bin Laden becomes While the terrorists who inflicted Tuesday s dreadful wound may have been inspired by him there is as yet no evidence that they were instructed by him Bin Laden s presumed guilt appears to rest on the supposition that he is the sort of man who would have done it But his culpability is irrelevant his usefulness to western governments lies in his power to terrify When billions of pounds of military spending are at stake rogue states and terrorist warlords become assets precisely because they are liabilities The need for dissent George Monbiot The Guardian Tuesday September 18 2001 This chapter describes JAPE a Java Annotation Patterns Engine JAPE provides finite state transduction over annotations based on regular expressions JAPE is a version of CPSL Common Pattern Specification Language JAPE allows you to recognise regular expressions in annotations on documents Hang on there s something wrong here a regular language can only describe sets of strings not graphs LA good description of the original version of this languag
194. s having Kleene applied to them and being labelled or is it a label Appelt s answer is that is always a label to get the other interpretation use This may be difficult for rule developers to remember JAPE disallows the label and makes all matched annotations available from every label JAPE adds a label operator which means that all the spans of any annotations matched are assigned to new annotations created on the RHS relative to that label With ordinary labels only the span of the outermost corners of the annotations matched is used This operator disappears in GATE version 2 with the elimination of multi span annotations Another problem regards RHS interpretation of unbound labels If we have something like Word string thing 1 Word string otherthing 2 on the LHS and references to 1 and 2 on the RHS only one of these will actually be bound to anything when the rule is fired The expression containing the other should be ignored In TextPro an assignment on the RHS that references an unbound label is evaluated to the value false In JAPE RHS expressions involving unbound operators are not evaluated B 5 Classes The main external interfaces to JAPE are the classes gate jape Batch and gate jape Compiler The CPSL Parser is implemented by ParseCpsl jj which is input to JavaCC and JJDoc to produce grammar documentation and finally Java
195. s higher priority than the matching positive rule The style of matching must be Appelt for this to work To create a negative rule simply state on the LHS of the rule the pattern that should NOT be matched and on the RHS do nothing In this way the positive rule cannot be fired if the negative pattern matches and vice versa which has the same end result as using a negative operator A useful variation for developers is to create a dummy annotation on the RHS of the negative rule rather than to do nothing and to give the dummy annotation a rule feature In this way it is obvious that the negative rule has fired Alternatively use Java code on the RHS to print a message when the rule fires An example of a matching negative and positive rule follows Here we want a rule which matches a surname followed by a comma and a set of initials But we want to specify that the initials shouldn t have the POS category PRP personal pronoun So we specify a negative rule that will fire if the PRP category exists thereby preventing the positive rule from firing Rule NotPersonReverse Priority 20 we don t want to match Jones I Token category NNP LN FAME SA a e pe fil A id e a os A VY ELULE NAL RE SES Token string Token category PRP Rule PersonReverse Priority 5 we want to match Jones F W Token category NNP Token string INITIALS person gt
196. s section describes how to load and run ANNIE see Chapter 7 from the development environment To embed ANNIE in other software see section 2 21 From the File menu select Load ANNIE system To run it in its default state choose With Defaults This will automatically load all the ANNIE resources and create a corpus pipeline called ANNIE with the correct resources selected in the right order and the default input and output annotation sets If Without Defaults is selected the same processing resources will be loaded but a popup window will appear for each resource which enables the user to specify a name and location for the resource This is exactly the same procedure as for loading a processing resource indi vidually the difference being that the system automatically selects those resources contained within ANNIE When the resources have been loaded a corpus pipeline called ANNIE will be created as before The next step is to add a corpus see Section 2 9 1 and select this corpus from the drop down Corpus menu in the Serial Application editor Finally click on Run from the Serial Application editor or by right clicking on the application name and selecting Run To view the results double click on the filename in the left hand pane 2 15 D Create and Edit Test Data Since many NLP algorithms require annotated corpora for training GATE s development environment provides easy to use and extendable facil
197. schema are created Another component allows unrestricted annota tions to be created Schemas are resources just like other GATE components Below we give some examples of such schemas Section 2 23 describes how to create new schemas TAREA O OO E TUNES EE AE A ee SS ES ARAS Date schema VMMAA AAA LANA AAA lt xml version 1 0 gt lt schema xmlns http www w3 org 2000 10 XMLSchema gt lt XSchema deffinition for Date gt lt element name Date gt lt complexType gt lt attribute name kind use optional gt lt simpleType gt lt restriction base string gt lt enumeration value date gt lt enumeration value time gt lt enumeration value dateTime gt lt restriction gt lt simpleType gt lt attribute gt lt complexType gt lt element gt lt schema gt VILA AAA AMIA A ADAN Person schema VILA AAA AAA ADAN lt xml version 1 0 7 gt lt schema xmlns http www w3 org 2000 10 XMLSchema gt lt XSchema definition for Person gt lt element name Person gt lt schema gt INIA AAA A AAA AAA AAN ANN Address schema IMAN AAA AAA AAA ADAN lt xml version 1 0 gt lt schema xmlns http www w3 org 2000 10 XMLSchema gt lt XSchema deffinition for Address gt lt element name Address gt lt complexType gt lt attribute name kind use optional gt lt simpleType gt lt restriction base string gt lt enumeration value email gt
198. sed GATE 1 and who miss the baroque mystery and labyrinthine elegance of the previous build structure may find it useful to note that drinking milk can ease the discomfort of an acid stomach You can also use a development environment like Borland JBuilder click on the gate jpx file but note that it s still advisable to use the Makefile to generate documentation the jar file and so on Also note that the run configurations have the location of a gate xml site configuration file hard coded into them so you may need to change these for your site 2 7 D F Create a New CREOLE Resource CREOLE resources are Java Beans see chapter 3 They come in three types Language Resource Processing Resource and Visual Resource see chapter 1 section 1 3 1 To create a new resource you need to write a Java class that implements GATE s beans model compile the class and any others that it uses into a Java Archive JAR file e write some XML configuration data for the new resource e tell GATE the URL of the new JAR and XML files The GATE development environment helps you with this process by creating a set of direc tories and files that implement a basic resource including a Java code file and a Makefile This process is called bootstrapping For example let s create a new component called GoldFish which will be a Processing Resource that looks for all instances of the word fish in a document and adds an annotation of ty
199. sh Grammar Prentice Hall Regents 1989 Baker et al 02 P Baker A Hardie T McEnery H Cunningham and R Gaizauskas EMILLE A 67 Million Word Corpus of Indic Languages Data Collection Mark up and Har monisation In Proceedings of 8rd Language Resources and Evaluation Conference LREC 2002 pages 819 825 2002 Bird amp Liberman 99 S Bird and M Liberman A Formal Framework for Linguistic Annotation Technical Report MS CIS 99 01 Department of Computer and Information Science University of Pennsylvania 1999 http xxx 1lanl gov abs cs CL 9903003 Bontcheva et al 00 K Bontcheva H Brugman A Russel P Wittenburg and H Cunningham An Experiment in Unifying Audio Visual and Textual Infrastructures for Language Pro cessing R amp D In Proceedings of the Workshop on Using Toolsets and Architectures To Build NLP Systems at COLING 2000 Luxembourg 2000 http gate ac uk Bontcheva et al 02a K Bontcheva H Cunningham V Tablan D Maynard and O Hamza Using GATE as an Environment for Teaching NLP In ACL Workshop on Effective Tools and Methodologies in Teaching NLP 2002 Bontcheva et al 02b K Bontcheva H Cunningham V Tablan D Maynard and H Saggion Developing Reusable and Robust Language Processing Components for Information Systems using GATE In rd International Workshop on Natural Language and Informa tion Systems NLIS 2002 Aix en Provence France 2002 IEEE Computer Society Press
200. she or category PRP string he e Try to locate a candidate in the text succeeding the quoted fragment first pattern If more than one candidate is present choose the closest to the end of the quote If a candidate is found then propose it as antecedent and exit e Try to locate candidate in the text preceding the quoted fragment third pattern Choose the closes one to the beginning of the quote If found then set as antecedent and exit e Try to locate antecedents in the unquoted part of the sentence preceding the sentence where the quote starts second pattern Give preference to the one closest to the end of the quote if any in the preceding sentence or closest to the sentence beginning 7 8 A Walk Through Example Let us take an example of a 3 stage procedure using the tokeniser gazetteer and named entity grammar Suppose we wish to recognise the phrase 800 000 US dollars as an entity of type Number with the feature money First of all we give an example of a grammar rule and corresponding macros for money which would recognise this type of pattern Macro MILLION_BILLION Token string m Token string million Token string b Token string billion Macro AMOUNT_NUMBER Token kind number Token string Token string Token kind number SpaceToken kind space MILLION_BILLION aS SEE O tO Se RO AE
201. signed plugins and maybe some warnings because of some missing plugin There are two ways of providing access to this directory when you use Prot g embedded in GATE The first is to copy the plugins directory in the current GATE directory This is the simple but not very clear way In this case if you add some plugin in your Prot g installation you should copy this directory every time to your GATE directory to get it there too The second way is to tell GATE where the Prot g installation directory ism so it can find the plugins subdirectory You can do this by setting the Java VM property Dprotege dir protege_installation_directory You can see more about this in Section 4 2 6 below 4 2 5 How to save a Prot g ontology in Ontotext ontology file format You can see and save a Prot g ontology in the Ontology Editor tab of the Prot g project LR GUI You have to switch from Prot g to the Ontology Editor tab and use the File jSave menu item The Prot g ontology will be stored in the URL given as the second parameter on creation of the Prot g project LR 4 2 6 Known problems and bugs e If you give only the filename on creation of Prot g project instead of full file name LN FAME PATO a e pe fil A fe MEA a os A VY LULE NAL RE SES There is a strange java lang NullPointerException when you try to load a Prot g project giving only a file name instead of the full file name You will have the sa
202. ssible Location is resolved by default to a person rather than a Location e g Ken London On the other hand a Chris tian name followed by a possible organisation ending is resolved to an Organisation e g Alexandra Pottery though this is a slightly less sure rule C 4 5 Contextual information Although most of the rules involving contextual information are invoked in a much later phase there are a few which are invoked here such as X joined Y where X is annotated as a Person and Y as an Organization This is so that both annotations types can be handled at once C 5 name_post jape This grammar runs after the name grammar to fix some erroneous annotations that may have been created Of course a more elegant solution would be not to create the problem in the first instance but this is a workaround For example if the surname of a Person contains certain stop words e g Mary And then only the first name should be recognised as a Person However it might be that the firstname is also an Organization and has been tagged with TempOrganization already e g U N If this is the case then the annotation is left untouched because this is correct ci VIM ANC ae Aeron a REE a eS PR a E iVe C 6 date_pre jape This grammar precedes the date phase because it includes extra context to prevent dates being recognised erroneously in the middle of longer expressions It mainly treats the case where an expressio
203. sts def is used to access these lists for each list a major type is specified and optionally a minor type In the example below the first column refers to the list name the second column to the major type and the third to the minor type These lists are compiled into finite state machines Any text tokens that are matched by these machines will be annotated with features specifying the major and minor types Grammar rules then specify the types to be identified in particular circumstances Each gazetteer list should reside in the same directory as the index file currency_prefix lst currency_unit pre_amount currency_unit lst currency_unit post_amount date lst date specific day lst date day So for example if a specific day needs to be identified the minor type day should be specified in the grammar in order to match only information about specific days if any kind of date needs to be identified the major type date should be specified to enable tokens annotated with any information about dates to be identified More information about this can be found in the following section Sit is also possible to include a language in the same way where lists for different languages are used though ANNIE is only concerned with monolingual recognition aS SEE O OO E UNES ES AE A ee SS IT ave 7 3 Sentence Splitter The sentence splitter is a cascade of finite state transducers which segments the text into sentences This module is
204. t lt Feature gt lt Annotation gt lt Annotation Type Sentence StartNode 0 EndNode 147 gt lt Annotation gt lt Annotation Type Split StartNode 146 EndNode 147 gt lt Feature gt lt Name className java lang String gt kind lt Name gt lt Value className java lang String gt internal lt Value gt lt Feature gt lt Annotation gt lt Annotation Type Lookup StartNode 11 EndNode 20 gt lt Feature gt lt Name className java lang String gt majorType lt Name gt lt Value className java lang String gt date_key lt Value gt lt Feature gt lt Annotation gt lt AnnotationSet gt lt Named annotation set gt lt AnnotationSet Name Original markups gt lt Annotation Type paragraph StartNode 0 EndNode 147 gt lt Annotation gt lt AnnotationSet gt lt GateDocument gt Note One must know that all features that are not collections containing numbers or strings or that are not numbers or strings are discarded With this option GATE does not preserve those features it cannot restore back The preserve format option This option is available in the GATE GUI from the popup menu of the annotations table If no annotation in this table is selected then the option will restore the document s original markup If certain annotations are selected then the option will attempt to restore the original markup and insert all the selected ones When an annotation violates the crossed over
205. t from the execution of the quoted speech and pleonastic it submodules The module works according to the following algorithm e Preprocess the current document This step locates the annotations that the submod ule need such as Sentence Token Person etc and prepares the appropriate data structures for them e For each pronoun do the following inspect the proper appropriate context for all candidate antecedents for this kind of pronoun choose the best antecedent if any e Create the coreference chains from the individual anaphor antecedent pairs and the coreference information supplied by the OrthoMatcher this step is performed from the main coreference module 7 7 4 Detailed description of the algorithm Full details of the pronominal coreference algorithm are as dollows Preprocessing The preprocessing task includes the following subtasks e Identifying the sentences in the document being processed The sentences are identified with the help of the Sentence annotations generated from the Sentence Splitter For each sentence a data structure is prepared that contains three lists The lists contain the annotations for the person organization location named entities appearing in the sentence The named entities in the sentence are identified with the help of the Person Location and Organization annotations that are already generated from the Named Entity Transducer and the OrthoMatcher LN FAME PATO a e pe fil A
206. tations matched on the LHS of a rule may be referred to on the RHS by means of labels that are attached to pattern elements At the beginning of each grammar several options can be set e Control this defines the method of rule matching see Section refsec jape priority e Debug when set to true if the grammar is running in Appelt mode and there is more than one possible match the conflicts will be displayed on the standard output See also Section 6 3 Input annotations must also be defined at the start of each grammar If no annotations are defined the default will be Token SpaceToken and Lookup i e only these annotations will be considered when attempting a match See Section 6 5 for more details There are 3 main ways in which the pattern can be specified e specify a string of text e g Token string of e specify an annotation previously assigned from a gazetteer tokeniser or other module e g Lookup e specify the attributes and values of an annotation e g Token kind number Macros can also be used in the LHS of rules This means that instead of expressing the information in the rule it is specified in a macro which can then be called in the rule The reason for this is simply to avoid having to repeat the same information in several rules Macros can themselves be used inside other macros The same operators can be used as for the tokeniser rules i e TAREA O TAE O E TUNES AE ee ee Re SS E
207. te A O B 3 Algorithms for JAPE Rule Application o o B 4 Label Binding Scheme 12d A A I Ree ee a A B 5 Classes B 6 Implementation o eur Gee eSB A EER Ee Ee ee B 7 Compilati OM ag at set eS ree E A Lat od SS Ai ob Oa ag es Sale C Named Entity State Machine Patterns C 1 Main jape C 2 first jape Cosy HES Ra meJaDe aria A a A e A id Ba CGA ane ape ee fare fa a CCAA aa 0 name Ost ape Ls da a a a ae C 6 date pre jaye gt s sus SS Bae do a ea Ra C 7 date jape Co feldat Jape rata AA SAA AAA AAA T C 9 number ja PES ASIA LA AAA ES AS a E a R CIO address Jape be Ge e A A A RR ORE SS C 11 url jape C 12 identifier APEC AR e e dt ok a aha GA obte ape td Mog Be A AAA ea a i C 14 final jape a A a ee I Se BO ee ai ak CAG mame Context Jape eusebio aoe ad oe Boe wd Se Sh a Gilt Ora context ape ere oa SSG ek A a OE Gee Oe eS Se a G18 locveantext japec tire e Det GK e AM ood Ole eck BAe a BSS C 19 clean jape References 121 122 123 125 126 126 134 134 134 137 140 141 143 144 150 150 151 154 155 155 156 157 157 158 159 159 159 159 160 160 160 160 160 161 161 161 162 162 162 Contents 1 Introduction Le How to Use Fhis Text ace ns ds ag Be wre tah ey rl ae 1 OODE Sisi ir Re de id dd ee ds Tey E O o A II E Gre amp Dat Sede Coa De Ard Boe 1 3 1 Developing and Deploying Language Processing Facilities 1 3 2 Built in Component
208. ted by a dot The rule for this is given below Rule IPAddress Token kind number Token string Token kind number Token string Token kind number Token string Token kind number ipAddress gt ipAddress Address kind ipAddress 3We might be more specific and state the possible lengths of the number but within the confines of this project we currently have no need to because there is no ambiguity with anything else A ia e a e e o a o Se A SS ee To identify a date or time there are many possible variations and so many rules are needed For example the same date information can appear in the following formats amongst oth ers Wed 10 7 00 Wed 10 July 00 Wed 10 July 2000 Wed 10th of July 2000 Wed July 10th 2000 Wed 10 July 2000 Different types of date can also be expressed For example the following would also be classified as date entities the late 80s Monday St Andrew s Day 99 BC mid November 1980 81 from March to April This also means there is a much greater potential for ambiguity For example many of the months of the year can also be girls Christian names e g May June This means that contextual information may be needed to disambiguate them or we may have to guess which is more likely based on frequency For example while Friday could be a person s name as in Man Friday it is much more likely to be a d
209. tell the system what character encoding the text is stored in By default GATE will set this parameter to be the empty string This tells Java to use the default encoding for whatever platform it is running on at the time e g on Western versions of Windoze this will be ISO 8859 1 and Eastern ones ISO 8859 9 A popular way to store Unicode documents is 15Tmplemented by Valentin Tablan Mark Leisher and Markus Kramer Initial version developed by Mark Leisher SS SE O OO UNES EE AE ee ee ee SS ee Vs in UTF 8 which is a superset of ASCII but can still store all Unicode data if you get an error message about document I O during reading try setting the encoding to UTF 8 or some other locally popular encoding To see a list of available encodings try opening a document in GATE s unicode editor you will be prompted to select an encoding 2 29 Work with Oracle GATE s Oracle layer is documented separately in http gate ac uk gate doc persistence pdf Note that running an Oracle installation is not for the faint hearted Chapter 3 CREOLE the GATE Component Model Noam Chomsky s answer in Secrets Lies and Democracy David Barsamian 1994 Odonian to What do you think about the Internet I think that there are good things about it but there are also aspects of it that concern and worry me This is an intuitive response I can t prove it but my feeling is that since people aren t Martians or ro
210. th the current set of resources This is done by selecting Store corpus for future evaluation from the Corpus Benchmark Tools Select the corpus to be processed from the top of the subdirectory structure ie the directory containing the marked and stored subdirectories If a processed subdirectory exists the results will be placed there if not one will be created Once the corpus has been processed the tool can be run in Default or Current mode The resulting HTML file will be output in the main GATE messages window This can then be pasted into a text editor and viewed in an internet browser for easier viewing The tool can be used either in verbose or non verbose mode by selecting the verbose option from the menu In verbose mode any score below the user s pre defined threshold stored in corpus_tool properties file will show the relevant annotations for that entity type thereby enabling the user to see where problems are occurring See section 2 19 3 for a description of the corpus_tool properties file 2 19 2 Standalone mode Alternatively the tool can be run in standalone mode using the following commands e To process the corpus issue the command aS Se O ot tO E UNES ES AE A ee SS ES gate e generate corpusname where corpusname is the name of the corpus e To run in Stored mode issue the command gate e verbose marked _stored corpusname e To run in Current mode issue the command gate e
211. the site wide level whose location is specified by the environment variable GATE_CONFIG or the Java property gate config e the user level which lives in the user s HOME directory on UNIX or their profile directory on Windoze note that parts of this file are overwritten by GATE when saving user settings gt These are either in gate classes or in gate jar depending on whether you have a binary or a built version SS SE O OO UNES EE Se eee eee ee SS ee Where configuration data appears on several different levels the more specific ones overwrite the more general This means that you can set defaults for all GATE users on your system for example and allow individual users to override those defaults without interfering with others When using the GATE development environment the side wide config file is chosen depend ing on the settings of GATE_CONFIG and GATE_HOME see section 2 2 Configuration data can be set from the GUI via the Options menu Configuration choice The user can change the appearance of the GUI via the Appearance submenu which includes the options of font and the look and feel The Advanced submenu enables the user to include annotation features when saving the document and preserving its format to save the selected Options automatically on exit and to save the session automatically on exit The Input Methods menu available via the Options menu enables the user to change the default la
212. theSum numberList This stuff then gets converted into code that is used to form the class we create for RHSs looking like this package japeactionclasses import gate import java io import gate jape import gate util import gate creole public class Test2NumberSumActionClass implements java io Serializable RhsAction public void doit Document doc LeftHandSide lhs AnnotationSet numberListAnnots hs getBoundAnnots numberList 1 if numberListAnnots size 0 4 int theSum 0 for int i 0 i lt numberListAnnots length i String numberString doc spanStrings numberListAnnots nth i try 1 theSum Integer parseInt numberString catch NumberFormatException e doc addAnnotation number numberListAnnots getLeftmostStart numberListAnnots getRightmostEnd sun new Integer theSum ci VINA ANC a Re aero toa avert B 7 Compilation JAPE uses a compiler that translates CPSL grammars to Java objects that target the GATE API and a regular expression library It uses a compiler compiler JavaCC to construct the parser for CPSL Because CPSL is a transducer based on a regular language in effect an FST it deploys similar techniques to those used in the lexical analysers of parser generators e g lex flex JavaCC tokenisation rules In other words the JAPE compiler is a compiler generated with the help of a compiler compiler which uses back end
213. their configu ration data is loaded to begin with the actual classes are loaded when the user requests the instantiation of a resource The backplane performs these functions e component discovery bootstrapping loading and reloading e management and visualisation of native data structures for common information types e generalised data storage and process execution A set of components plus the framework is a deployment unit which can be embedded in another application The key task of the development environment is to facilitate constructing components and viewing and measuring their results TAREA O OO E UNES AE A ee Se ee 3 4 Language Resources and Processing Resources This section describes in more detail the Language Resource and Processing Resource termi nology introduced earlier If you re happy with these terms you can safely skip this section Like other software LE programs consist of data and algorithms The current orthodoxy in software development is to model both data and algorithms together as objects Systems that adopt the new approach are referred to as Object Oriented OO and there are good reasons to believe that OO software is easier to build and maintain than other varieties Booch 94 Yourdon 96 In the domain of human language processing R amp D however the terminology is a little more complex Language data in various forms is of such significance in the field that it is frequently worked on i
214. tion index html For licence information please refer to www ontotext com gate gazetteer documentation licence ontotext html or to licence ontotext html in the lib folder of GATE lt COMMENT gt lt PARAMETER NAME document RUNTIME true COMMENT The document to be processed gt gate Document lt PARAMETER gt lt PARAMETER NAME annotationSetName RUNTIME true COMMENT The annotation set to be used for the generated annotations OPTIONAL true gt java lang String lt PARAMETER gt lt PARAMETER NAME 1istsURL DEFAULT gate creole gazeteer default lists def COMMENT The URL to the file with list of Lv A bi e E pe fil A id e a os A VY ELULE NAL RE SES LLY lists SUFFIXES def gt java net URL lt PARAMETER gt lt PARAMETER DEFAULT UTF 8 NAME encoding COMMENT The encoding used for reading the definitions gt java lang String lt PARAMETER gt lt PARAMETER DEFAULT true NAME caseSensitive COMMENT Should this gazetteer diferentiate on case Currently the Gazetteer works only in case sensitive mode gt java lang Boolean lt PARAMETER gt lt ICON gt shefGazetteer gif lt ICON gt lt RESOURCE gt 8 5 Flexible Exporter The Flexible Exporter enables the user to save a document in its original format with added annotations The user can select the name of the annotation set from which these annotations are to be found which annotations from this set are to be included whether features are to be included and v
215. tp www dcs shef ac uk nlp gate Cunningham et al 96b H Cunningham K Humphreys R Gaizauskas and Y Wilks TIPSTER Compatible Projects at Sheffield In Advances in Text Processing TIPSTER Pro gram Phase I DARPA Morgan Kaufmann California 1996 Cunningham et al 96c H Cunningham Y Wilks and R Gaizauskas GATE a General Architecture for Text Engineering In Proceedings of the 16th Conference on Computational Linguis tics COLING 96 Copenhagen August 1996 Cunningham et al 96d H Cunningham Y Wilks and R Gaizauskas Software Infrastructure for Language Engineering In Proceedings of the AISB Workshop on Language Engineering for Document Analysis and Recognition Brighton U K April 1996 Cunningham et al 96e H Cunningham Y Wilks and R Gaizauskas New Methods Current Trends and Software Infrastructure for NLP In Proceedings of the Conference on New Methods in Natural Language Processing NeMLaP 2 Bilkent University Turkey September 1996 http xxx lanl gov abs cs CL 9607025 FUN EVE tive iv v Cunningham et al 97a H Cunningham K Humphreys R Gaizauskas and Y Wilks GATE a TIPSTER based General Architecture for Text Engineering In Proceedings of the TIPSTER Text Program Phase III 6 Month Workshop DARPA Morgan Kaufmann Califor nia May 1997 Cunningham et al 97b H Cunningham K Humphreys R Gaizauskas and Y Wilks Software Infrastruc ture for Natural Langua
216. transparent soul and they talked about those secret regions for which they felt hardly any longing yet towards which the irrational wings of fate might one day drive them if only in their dreams For however much they might belong to one another heart and soul they knew last night was not the first time they had been stirred by a whiff of freedom danger and adventure Dream Story Arthur Schnitzler 1926 pp 4 5 GATE was originally developed in the context of Information Extraction IE R amp D and IE systems in many languages and shapes and sizes have been created using GATE with the IE components that have been distributed with it see Maynard et al 00 for descriptions http gate ac uk ie 99 LN PERL HPA do Aai A a AAA mi mica YY LULE NALS ivVv of some of these projects GATE is distributed with an IE system called ANNIE A Nearly New IE system devel oped by Hamish Cunningham Valentin Tablan Diana Maynard Kalina Bontcheva Marin Dimitrov and others ANNIE relies on finite state algorithms and the JAPE language see chapter 6 ANNIE components form a pipeline which appears in figure 7 1 ANNIE components are Document format ANNIE A HTML SGML email v o IE modules Input p gt GATE Be URL or text Document A a x A Character y Semantic JAPE IE Unicode Grammar Tokeniser Class Sequence Tagger C d ascade C Rules E ie e v a Ortho NOTE square boxes are FS Ga
217. ue to be the same There are three basic options to select e To take all the features from the key set into consideration e To take only the user selected ones e To ignore all the features from the key set If false positives are to be measured select the annotation type and relevant annotation set to be used as the denominator normally Token or Sentence The weight for the F Measure can also be changed by default it is set to 0 5 i e to give precision and recall equal weight Finally click on Evaluate to display the results Note that the window may need to be resized manually by dragging the window edges or internal bars as appropriate In the main window the key and response annotations will be displayed They can be sorted by any category by clicking on the relevant column header The key and response annotations will be aligned if their indices are identical and are color coded according to the legend displayed Precision recall F measure and false positives are also displayed below the annotation tables each according to 3 criteria strict lenient and average See sections 9 1 and 9 4 for more details about the evaluation metrics SS SE O AEREO E UNES ES AE A eee ee SS IT 2 18 2 API Since Annotation Diff is a tool not a processing resource it needs to be constructed directly via its constructor Then all its parameters are set using the respective mutator methods Le setXXX and finally the init
218. ule The quoted speech submodule identifies quoted fragments in the text being analysed The identified fragments are used by the pronominal coreference submodule for the proper res olution of pronouns such as I me my etc which appear in quoted speech fragments The module produces Quoted Text annotations The submodule itself is a JAPE transducer which loads a JAPE grammar and builds an FSM over it The FSM is intended to match the quoted fragments and generate appropriate annotations that will be used later by the pronominal module The JAPE grammar consists of only four rules which create temporary annotations for all punctuation marks that may enclose quoted speech such as etc These rules then try to identify fragments enclosed by such punctuation Finally all temporary annotations gen erated during the processing except the ones of type Quoted Text are removed because no other module will need them later iw t MEM PATO Seto pe fil i a MMA a os A VY LULE NAL RE SES ive 7 7 2 Pleonastic It submodule The pleonastic it submodule matches pleonastic occurrences of it Similar to the quoted speech submodule it is a JAPE transducer operating with a grammar containing patterns that match the most commonly observed pleonastic it constructs 7 7 3 Pronominal Resolution Submodule The main functionality of the coreference resolution module is in the pronominal resolution submodule This uses the resul
219. umber of any type of entity the results for that entity type can become skewed Compare the impact on precision of one error where the total number of correct entities 1 and one error where the total 100 Assuming the document length is the same then the false positive score for each text on the other hand should be identical Common metrics for evaluation of IE systems are defined as follows Correct 1 2Partial Precision gt 9 1 reason Correct Spurious 1 2Partial an Correct 1 2Partial Recall i 1 9 2 Correct Missing 1 2Partial ma 82 1 PxR F measure 9 3 PR P oy where is a value between 0 and 1 reflecting the weighting of P vs R If is set to 0 5 the two are weighted equally Spurious FalsePositive 9 4 C where c is some constant independent from document richness e g the number of tokens or sentences in the document Note that we consider annotations to be partially correct if the entity type is correct and the spans are overlapping but not identical Partially correct responses are normally allocated a half weight 1By this we mean the relative number of entities of each type to be found in a set of documents Chapter 10 Users Groups and LR Access Rights Well he said it s to do with the project which first made the software incar nation of the company profitable It was called Reason and in its own way it was sensational What was it
220. urposes and can also be used as input for additional grammars or processing resources C 16 name_context jape This grammar looks for Unknown annotations occurring in certain contexts which indicate they might belong to Person This is a typical example of a grammar that would benefit from learning or automatic context generation because useful contexts are a hard to find manually and may require large volumes of training data and b often very domain specific In this core grammar we confine the use of contexts to fairly general uses since this grammar should not be domain dependent C 17 org _context jape This grammar operates on a similar principle to name_context jape It is slightly oriented towards business texts so does not quite fulfil the generality criteria of the previous grammar It does however provide some insight into more detailed use of contexts p A LLAVE tive ive C 18 loc_context jape This grammar also operates in a similar manner to the preceding two using general context such as coordinated pairs of locations and hyponymic types of information C 19 clean jape This grammar comes last of all and simply aims to clean up remove some of the temporary annotations that may not have been deleted along the way References Appelt 99 D Appelt An Introduction to Information Extraction Artificial Intelligence Com munications 12 3 161 172 1999 Azar 89 S Azar Understanding and Using Engli
221. user does not belong to any group In order to view all groups which are available you need to switch the tool to a Users for groups mode by clicking on the corresponding radio button This will switch the tool to showing the list of all groups in the left panel When you select a given group then the right panel shows all users who belong to that group see Figure 10 2 Suahili Group hamish English Language Group kalina ADMINS Create new group diana Delete group valentin Add user Remove user Rename group ea Figure 10 2 The tool in a group administration mode User manipulation Users are manipulated by selecting a user in the list of users and right clicking on it to see the user manipulation menu This menu allows the following actions iw f MEM PATO Seto pe fil i a MMA ai os E dS VY LULE NAL RE SES Uv Create new user shows a dialog where the user name and password of the new user must be specified Delete user delete the currently selected user Add to group shows a dialog displaying all available groups Select one to add the user to it Remove from group in the given dialog choose the group from which the user is to be removed Change password shows a dialog where the new password can be specified Rename user choose another name for the selected user All changes are automatically written to the Oracle database Group manipulation Groups are manipulat
222. va Beans component architecture with the addition of component metadata automated loading and standardised storage threading and distribution All components inherit from Resource via one of e LanguageResource LR represents entities such as lexicons corpora or ontologies e VisualResource VR represents visualisation and editing components that participate in GUIs e ProcessingResource PR represents entities that are primarily algorithmic such as parsers generators or ngram modellers dc See eN ivv A 1 2 Model view controller According to Buschmann et al Pattern Oriented Software Architecture 1996 the Model View Controller MVC pattern divides an interactive application into three components The model con tains the core functionality and data Views display information to the user Controllers handle user input Views and controllers together comprise the user interface A change propagation mechanism ensures consistency between the user interface and the model p 125 A variant of MVC the Document View pattern relaxes the separation of view and controller The View component of Document View combines the responsibilities of controller and view in MVC and implements the user interface of the system A benefit of both arrangements is that loose coupling of the document and view components enables multiple si multaneous synchronized but different views of the same document Geary Graphic
223. ve categories Number A number is defined as any combination of consecutive digits There are no subdivisions of numbers Symbol Two types of symbol are defined currency symbol e g and symbol e g amp gt These are represented by any number of consecutive currency or other symbols respectively Punctuation Three types of punctuation are defined start_punctuation e g end_punctuation e g and other punctuation e g Each punctuation symbol is a separate token SpaceToken White spaces are divided into two types of SpaceToken space and control according to whether they are pure space characters or control characters Any contiguous and homoge nous set of space or control characters is defined as a SpaceToken The above description applies to the default tokeniser However alternative tokenisers can be created if necessary The choice of tokeniser is then determined at the time of text processing iw o MEM PATO SSO pe fil i a MMA a os A VY LULE NAL RE SES UD 7 2 Gazetteer The gazetteer lists used are plain text files with one entry per line Each list represents a set of names such as names of cities organisations days of the week etc Below is a small section of the list for units of currency Ecu European Currency Units FFr Fr German mark German marks New Taiwan dollar New Taiwan dollars NT dollar NT dollars An index file li
224. ver you specify Tokens but not SpaceTokens in the Input SpaceTokens do not have to be mentioned in the pattern to be recognised If for example there is only one rule in a phase that requires SpaceTokens to be specified it may be judicious to move that rule to a separate phase where the SpaceToken can be specified as Input Chapter 7 ANNIE a Nearly New Information Extraction System And so the time had passed predictably and soberly enough in work and routine chores and the events of the previous night from first to last had faded and only now that both their days work was over the child asleep and no further distur bance anticipated did the shadowy figures from the masked ball the melancholy stranger and the dominoes in red revive and those trivial encounters became magically and painfully interfused with the treacherous illusion of missed oppor tunities Innocent yet ominous questions and vague ambiguous answers passed to and fro between them and as neither of them doubted the other s absolute candour both felt the need for mild revenge They exaggerated the extent to which their masked partners had attracted them made fun of the jealous stirrings the other revealed and lied dismissively about their own Yet this light banter about the trivial adventures of the previous night led to more serious discussion of those hidden scarcely admitted desires which are apt to raise dark and per ilous storms even in the pureset most
225. verbose marked _clean corpusname e To run in Default mode issue the command gate e verbose corpusname The tool can be run in verbose mode for any of these options using the verbose flag The results can be piped to an html file and viewed with an internet browser 2 19 3 How to define the properties of the benchmark tool The properties of the benchmark tool are defined in the file corpus_tool properties which should be located in the firectory from which Gate is run The file specifies the location for each processing resource to be used in the application The pathname should have the same format as that used when specifying the location of a resource n the GUI e g tokeniserRulesURL gate creole tokeniser DefaultTokeniser rules or tokeniserRulesURL file user gate src gate resources creole tokeniser DefaultTokeniser Other parameters which can be set here are e whether the gazetteer is case sensitive or not e the name of the textTag for the Annotation Set Transfer if used e the threshold for the verbose mode by default this is set to 0 5 e the name of the annotation set containing the human marked annotations TAE O OO E UNES EE AE ee eee Re SS IT 2 20 D Write JAPE Grammars JAPE is a language for writing regular expressions over annotations and for using patterns matched in this way as the basis for creating more annotations JAPE rules compile into finite state machines GATE s built in Information Extracti
226. w the pane containing the other annotations These annotations can be viewed in exactly the same way Descriptions of the annotations are simultaneously displayed in the bottom pane These lists can be sorted in ascending and descending order by any column by clicking on the corresponding column heading An arrow will appear indicating the direction of the sorting Clicking on an entry in the table will also highlight the respective matching text portion Right clicking on some part of the text in the main window will bring up a box containing a list of the annotations associated with it Selecting one of these annotation types will highlight the relevant annotation description in the lower pane if present If not present because the corresponding annotation on the right hand pane has not been selected this annotation on the right will then be automatically selected and all relevant text in the main A ee OO e o Se eee eee eRe SS ee window will be appropriately highlighted Although there is no cursor displayed in the various windows they can all be scrolled using the keyboard arrows as well as by using the scrollbars At any time the main viewer can also be used to display other information such as Messages by clicking on the header at the top of the main window If an error occurs in processing the messages tab will flash red and an additional popup error message may also occur 2 14 Dj Do Information Extraction with ANNIE Thi
227. ypes The list of annotation names must be in the same order as the corresponding annotation types in annotation Types e outputfileUrl this enables the user to select a different name for the output file The file will be stored in the same directory as the original source file Annotation Set Transfer The Annotation Set Transfer has no loadtime parameters It has the following runtime parameters e inputASName this defines the annotation set which is to be transferred If nothing is specified the Default annotation set will be used e outputASName this defines the new annotation set which will contain the transferred annotations The default for this is a set called Filtered e tagASName this defines the annotation set which contains the annotation covering the relevant part of the document to be transferred e textTagName this defines the name of the annotation covering the relevant part of the document to be transferred For example suppose we wish to perform named entity recognition on only the text covered by the BODY annotation from the Original Markups annotation set in an HTML document We have to run the gazetteer and tokeniser on the entire document because since these resources do not depend on any other annotations we cannot specify an input annotation TAREA O OO Se RO AE ee eee Re SS ee set for them to use We therefore transfer these annotations to a new annotation set Filtered and then perform the NE rec
228. zetteer Lists Matcher processes rounded ones are Lookup A data a N Pronominal Sentence 4 JAPE Sentence Cot terericer d JAPE Grammar A A Splitter Patterns lt Brill Rules ee gt GATE Document gt Tagger L Lexicon J XML dump of f A Output IE Annotations l Figure 7 1 ANNIE and LaSIE included with GATE though the linguistic resources they rely on are generally more simple than the ones we use in house The rest of this chapter describes these components 7 1 Tokeniser The tokeniser splits the text into very simple tokens such as numbers punctuation and words of different types For example we distinguish between words in uppercase and lowercase 2The principal architects of the IE systems in GATE version 1 were Robert Gaizauskas and Kevin Humphreys This work lives on in the LaSIE system A derivative of LaSIE was distributed with GATE version 1 under the name VIE a Vanilla IE system SS SE O OO E UNES EE SE A ee SS ee UA and between certain types of punctuation The aim is to limit the work of the tokeniser to maximise efficiency and enable greater flexibility by placing the burden on the grammar rules which are more adaptable 7 1 1 Tokeniser Rules A rule has a left hand side LHS and a right hand side RHS The LHS is a regular expression which has to be matched on the input the RHS describes the annotations to be added to the AnnotationSet The LHS is separated f

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