Introduction to the platform and Dislog language
Contents
1. The same thing is necessary for multiple occurences plus adv T E1 S adv T1 _ E1 E2 plus adv T2 _ E2 S conc T S El plus adv T _ S S This portion of code must be duplicated for all relevant symbols A higher order encoding could have been realized but 1t does affect efficiency quite substantially Constraints presented in chapter 1 must also be declared at least one instance to avoid failures a few examples are provided here exclut_unite title termin lt condition gt 1 lt condition gt 1 termin lt purpose gt lt purpose gt termin lt circumstance gt lt circumstance gt termin lt restatement gt lt xrestatement gt dom instr eng but condopt eng restatement non_dom instr eng avt cons A few constraints are given in the decl pl file as examples These must be kept to avoid system failures This file also contains the cascade declaration as explained below 3 4 4 Lexical data Lexical data is specified in the lexique pl file Lexical data can follow the stardard categories and features of linguistic theories or be ad hoc depending on specific situations Lexical data is given in DCG format You have to design yourself you own lexicon In this first version we simply provide a few examples However2. goal gt 3 4 5 Other resources This first version is relatively limited and does not contain so many additional tools and facilities These will come in the second version of the tool However it contains the kernel necessary to implement the recognition of most discourse structures and to bind them The file gram pl contains a few grammar rules written in DCG style and compatible with the symbol format given above Indeed it is possible to have symbols in rules which are non terminal and which are associated with a grammar in that module np A B _ E S det A _ E S1 n B _ S1 S E np A _ E S S pro A _ Pach ae This short sample of a grammar for nps can be used in Dislog rules as such Note that the first argument of the np symbols contains the string of words which have been processed This argument could include any other form e g a normalized form or a tree 29 3 4 6 Input Output management The input output file management is realized in several files The main file is es p1 which contains the main calls dynamically produces names for output and intermediate files to avoid conflicts between parses and produces two kinds of displays a file for further processing which is a basic XML file and a similar file where structures get a color for easier reading This latter file can be read by various XML editors File names are created dynamically demo out html tags no colors and demo c
3. 20 Rosner D Stede M 1992 Customizing RST for the Automatic Production of Technical Manuals in R Dale E Hovy D Rosner and O Stock eds Aspects of Automated Natu ral Language Generation Lecture Notes in Artificial Intelligence pp 199 214 Springler Verlag 21 Saaba A Sawamura H 2008 Argument Mining Using Highly Structured Argument Repertoire proceedings EDMOS Niigata 22 Saito M Yamamoto K Sekine S 2006 Using Phrasal Patterns to Identify Discourse Relations ACL 06 23 Saint Dizier P 1994 Advanced Logic programming for language processing Academic Press 24 Saint Dizier P 2012 Processing Natural Language Arguments with the lt TextCoop gt Platform Journal of Argumentation and Computation vol 3 1 25 Takechi M Tokunaga T Matsumoto Y Tanaka H 2003 Feature Selection in Catego rizing Procedural Expressions The Sixth International Workshop on Information Retrieval with Asian Languages IRAL2003 pp 49 56 26 Van Dijk T A 1980 Macrostructures Hillsdale NJ Lawrence Erlbaum Associates 27 Webber B 2004 D LTAG extending lexicalized TAGs to Discourse Cognitive Science 28 pp 751 779 Elsevier 28 Wierzbicka A 1987 English Speech Act Verbs Academic Press 33
4. More complex representations e g based on primitives can be computed using a rich semantic lexicon 1 2 3 The structure of Dislog rules Let us now introduce in more depth the structure of Dislog rules Dislog follows the prin ciples of logic based grammars as implemented three decades ago in a series of formalisms among which most notably Definite Clause Grammars Pereira and Warren 1981 Meta morphosis Grammars Colmerauer 1978 and Extraposition Grammars Pereira 1981 These formalisms were all designed for sentence parsing with an implementation in Prolog via a meta interpreter or a direct translation into Prolog Saint Dizier 1994 The last two forma lisms include a device to deal with long distance dependencies Dislog adapts and extends these grammar formalisms to discourse processing it also ex tends the regular expression format which is often used as a basis in language processing tools The rule system of Dislog is viewed as a set of productive principles A rule in Dislog has the following general form which is globally quite close to Definite Clause Grammars in its spirit L Representation R P where 1 Lis a non terminal symbol 2 Representation is the representation resulting from the analysis it is in general the ori ginal text with XML tags that annotates the discourse structures It can also be a partial dependency structure or a more formal representation This field is indeed totally open an
5. we are developing sets of lexical markers and other resources which are useful for discourse analysis These will be made available in a second version Here are a few examples included into this version 28 o pronouns pro we _ gt we pro you _ gt you o goal connectors connecteur in order to goal gt in order to connecteur in order that goal gt in order that connecteur so goal gt so connecteur so as to goal gt so as to o a few specific marks describing the beginning of a sentence mdeb debph _ gt debph internal mark mdeb lt li gt _ gt lt 1i gt mdeb 1 _ gt 1 o condition expr if cond gt if o specific marks for reformulation expr reform in other words gt in other words expr_reform to put it another way _ gt to put it another way expr_reform that is to say _ gt that is to say tag lexical data this is useful for rules which basically bind structures on the basis of already produced tags each elements has a type specified in the second argument do Je de balise lt instruction gt instruction gt lt instruction gt balise lt instruction gt endinstruction gt lt instruction gt balise lt goal gt goal gt lt goal gt balise lt goal gt endgoal gt lt
6. a kernel It does contain the reasoning facilities module but at that moment no predefined function is given but the user may include his own a well developed environment and a rule management system that would e g control the rule syntax optimize them or translate them into the Dislog format These modules will come with a second release of the code which will be made available later However the system can be used as it is in this version It has in fact been used by several of our students in linguistics with little knowledge in computer science and in Prolog in particular The lt TextCoop gt platform and Dislog language run on different systems in particular Windows and Linux Prolog e g SWI Prolog must be installed first A basic knowledge of Prolog is preferable syntax and execution schema We suggest the reader to first read Chapters 1 and 2 of this document which give some foundations and background Chapter 3 essentially describes how to use the main features of Dislog and TextCoop We are aware that this first version has many imperfections and limitations However its use in several large size industrial applications shows that it is viable We thank our users in advance for any comments questions and suggestions they could send us Linguistic resources which are provided here are rather simple and given for the purpose of illustration We have however developed much larger sets of rules in particular around argument
7. enough to allow for the production of other types of representations such as dependencies 2 1 Instruction Definition An instruction in a procedure is a statement often in an imperative form that asks to realize an action This action can possibly be associated with various elements such as instruments equipments manners etc The main verb of an instruction is often in the imperative or infinitive form in French in the infinitive form in English A few structures Advice gt gap not neg verb action infinitive gap eos This chapter is a part of a paper presented at LREC 2012 Bourse and Saint Dizier 15 gap not neg verb faire gap verb action infinitive gap eos infinitive denotes a veb in the infinitive form without to faire is a light verb in French action denotes an action verb which is in general domain dependent eos denotes end of sentence via a punctuation mark or any other mark Resources Besides modals and a few terms like pronouns the main resource is a list of action verbs However in most cases only a limited set of verbs is needed about 100 Example Write titles in bold font 2 2 Advice Definition Relation between a conclusion and a support the conclusion invites the reader to perform an optional action to obtain better results while the support gives a motivation for realizing this action Structures Advice verb p
8. gt gap G4 eos which binds a warning conclusion with a warning support then the result is a warning repre sented by the following XML structure lt warning gt lt warning concl gt G1 lt warning concl gt G2 lt warning supp gt G3 lt warning supp gt G4 lt warning gt 3 4 3 Parameters and Structure Declarations In contrast with Prolog but with the aim of improving efficiency it is necessary in Dislog to declare a few elements This is realized in the dec1 p1 file A number of standard symbols are already declared but check that yours are indeed declared otherwise your rules will fail First in order to allow for a proper variable binding any symbol used in rules must be declared as follows tt adv Mot Restr E S E S tt adj Mot Restr E S E S tt neg Mot Restr E S E S 27 tt np Mot Restr E S E S tt det Mot Restr E S E S In this example the symbols adv adj neg np and det are declared The co occurence of the symbols E and S allows to bind the variables of the symbols with the string of words to process in the meta interpretor the TextCoop engine Similarly any symbol which can be optional must be declared by means of a piece of code which must be reproduced from the following schema which encodes the optionality for auxiliaries opt aux AUX A E1 E2 aux AUX A El E2 opt aux _ E E
9. illustrations The rule sample given here is not in Dislog it is in a readable form convenient for linguistic analysis These rules must then be translated into Dislog Chapter 3 For the time being there is no automatic translator as it is the case e g for DCGs therefore a manual encoding must be carried out by the programmer This is however a rather simple task and will be available in the next release In the next chapter we show how these rules are implemented in Dislog For each discourse relation a definition is given in addition to the examples of rules and resources Rules are designed for French or English depending on cases In general structures are relatively similar Then some linguistic realizations of discourse relations coming from our corpus of English didactic texts or procedures are provided The curly brackets show that an element is optional Resources given here are samples The rules presented below have been produced manually from a manual analysis of dis course structures over a development corpus We feel this is the best way to proceed for dis course structures However rules can result from various types of statistical analysis including a variety of learning methods Dislog is general enough to allow various forms of encodings Similarly in the next chapter we show how to produce representations based on XML tags This is probably the simplest representation which can be produced However Dislog is flexible
10. of a binding rule Warnings are composed of a conclu sion and a support not developed above These two structures are recognized separetely by dedicated rules Then it is necessary to bind these two structures to get a warning Let us assume that both supports and conslusions are explicitly tagged then a simple binding rule is Warning lt warning concl gt gap G1 lt warning concl gt gap G2 lt warning supp gt gap G3 lt warning supp gt gap G4 eos Then the whole structure is tagged e g lt warning gt Similar rules are defined to bind nucleus with their related satellites G1 G2 G3 and G4 are variables that represent the contents list of words skipped by each gap For different gaps variables must be different Binding rules can be more complex than this example but the principle remains the same 2 5 Cause Definition Relation where segment B traditionally called the antecedent provokes the realization of an event A the consequent Structures Cause conn cause gap G ponct comma conn cause gap G eos Resources conn cause because because of on account of ponct comma Examples Because books are so thorough and long you have to learn to skim Long lists result in shallow essays because you don t have space to fully explore an idea Many poorly crafted essays have been produced on account of a lack of preparation and confi dence 2 6 Condition Definition
11. out html tags and colors If you have sufficient Prolog programming skills you can modify this file e g changing colors as you need it It is important to note that in this first version structure processing is realized on a sen tence basis We have improved and parameterized this situation which is somewhat limited an extension is available on demand Meanwhile you can end the text portions you want to process by a dot and replace dots ending sentences in these portions by another symbol e g the word dot which can be re written later by a real dot in the output file Basically input files must be plain text possibly with XML marks Word files cannot be processed It must also be noted that Prolog has some difficulties with UTF8 encoded texts The two other files for input output operations are internal to the system and should not be modified lire pl reads files under various formats and produces a list of words which is the entry for the main processing In this module some characters are transformed into different codes in order to avoid any interference with Prolog predefined elements These are then restored in their original form when the final output form is produced This is an important issue to keep in mind since some elements in teh elxicon must take these aspects into account The module functions pl contains a variety of basic utilities which you may use for various purposes besides the present software 3
12. the system performances Issues related to the rule system size and complexity Two parameters related to the rule system are investigated here how much the number of rules and the rule size impact the efficiency The results obtained concerning the number of rules are the following 20 29 A O IEA CIN E ICI Fig 2 Impact of number of rules As can be noted increasing the number of rules has a moderate impact on performances one of the reasons is that the most prototypical rules are executed first Rules have here an average complexity 4 symbols and a gap in average and an average of 8 rules per cluster Lexical size here is fixed 500 entries 20 rules is a very small system while 80 to 120 rules is a standard size for an application The results we obtain are difficult to accurately analyze besides rule ordering considerations results depend on the distribution of rules per cluster and the form of the rules For example the presence of non ambiguous linguistic markers at the beginning of a rule enhances rule selection and therefore improves efficiency Constraints such as those presented above are also very costly since they are checked at each step of the parsing process for the structures at stake Selective binding rules have little impact on efficiency their first symbol being an XML tag backtracking occurs at an early stage of the rule traversal Let us now consider rule size which is obviously an important feature rul
13. 5 Execution schema and structure of control The lt TextCoop gt engine is a meta interpretor written in standard Prolog This is a well know technique in Logic Programming which is very convenient for developing e g alternative processing strategies or demonstrators The strategy implemented in lt TextCoop gt is quite similar to the Prolog strategy However there are some major differences you need to be aware of The lt TextCoop gt engine will consider for a given text rule clusters one after the other Therefore rule clusters must all be organized in a cascade that describes the cluster execution order It must be declared by a cascade as an automata cascade in the dec1 p1 file as follows cascade eng circ eng condition eng purpos ng restat ng illus eng The whole text is inspected for each rule cluster one after the other If a cluster does not produces any result then the next one is activated there is no failure In case you wish to define several cascades the first argument of the cascade predicate is its identifier eng here The maximal length of a cascade is 60 elements which sounds quite large During execution you can see in the Prolog window the different steps with the intermediate files being compiled 30 Within a cluster rules are considered one after the other from the first to the last one similarly to Prolog strategy However there is here a major difference The string of words to proces
14. Introduction to the lt TextCoop gt platform and Dislog language User Manual V 1 Patrick Saint Dizier IRIT CNRS 118 route de Narbonne 31062 Toulouse Cedex France stdizier irit fr April 2012 Table des mati res 1 Foundational Aspects of lt TextCoop gt and Dislog 1 1 The Challenges ss su ele Raga how ee GR ee Ah E MAO Go 1 2 The lt TextCoop gt platform and the Dislog language 1 2 1 Some linguistic considerations o 1 2 2 Some foundational principles of lt TextCoop gt 1 2 3 The structure of Dislog rules o o 1 2 4 Dislog advanced features o e 1 2 5 Introducing reasoning aspects into discourse analysis 1 2 6 Processing complex constructions the case of Dislocation construc HONS A A Words SP ere Rae Moe ae EN e nata 1 3 The lt TextCoop gt engine 2 0 2002 0 ee ee ee 1 3 1 System performances and discussion 00 1 3 2 The lt TextCoop gt environment 000 Writing Dislog rules 2 SCAN rita aie E Agar ee awe Boe PR A A o sao dy O A da c o Sas Gaim ee ESA ate a Dai O eG A en 2 3 Warnin A Ge SON pa Sek Sek UBE Sele Sd War BAL aE a 2 4 Binding rules for warnings 0 0000020 2 eee TOT Cause e at Bos ee a Mek ewe a at a ta AAA ee a A 2 6 Condi oN 2 5 8204 2e ek AAA oo thes on Pe ee Es AT CONCESSION luis Renae a des RO a Be AL be A ee
15. Relation where the segment B refers to a situation which is necessary for A to be realized Structures 17 Condition conn cond gap G ponct comma conn cond gap G eos Resources conn cond if Examples If all of the sources seem to be written by the same person or group of people you must again seriously consider the validity of the topic If you put too many different themes into one body paragraph then the essay becomes confu sing For essay conclusions don t be afraid to be short and sweet if you feel that the argument s been well made 2 7 Concession Definition Relation where the segment B contradicts part of the segment A or contradicts the implicit conclusion which can be drawn from segment A Structures Concession conn opposition alth gap 1 ponct comma gap G2 eos G conn opposition_alth gap G eos conn opposition how gap G Resources conn opposition_alth although though even though even if notwithstanding despite in spite of conn opposition_how however Examples An essay can be immaculately written organized and researched however without a conclusion the reader is left dumbfounded frustrated confused Though the word essay has come to be understood as a type of writing in Modern English its eos origins provide us with some useful insights Your paper should expose some new idea or insight about the topic not jus
16. ation and explanation structures These can be made available upon request However a good knowledge of the tool is necessary for their full understanding Chapitre 1 Foundational Aspects of lt TextCoop gt and Dislog This chapter summarizes the concepts at the basis of Dislog and how they are interpreted in the lt TextCoop gt engine The strategy adopted in Dislog is that basic discourse units and functions e g illustration are recognized by means of rules Then with the same formalism another device bounding rules is introduced to bind basic units into larger structures There is no theoretical approach taken a priori on discourse analysis in Dislog It is an hopefully convenient logic based programming language to recognize discourse structures in texts Fi nally Dislog includes a variety of devices to express constraints and to introduce knowledge and reasoning These elements are introduced hereafter Chapter 2 develops the linguistic analysis of a number of discourse functions while Chap ter 3 shows how these are implemented in Dislog 1 1 The Challenges Discourse analysis is a very challenging task because of the large diversity of discourse structures the various forms they take in language and the potential knowledge needs for their identification Rhetorical structure theory RST Mann el al 1988 is a major attempt to orga nize investigations in discourse analysis with the definition of 22 basic structures Since the
17. ational and precision perspectives we ve already discussed 20 2 14 The Art of writing Dislog rules and constraints The ease of writing rules and the natural character of those rules with respect to language and corpus observations are major properties that any rule system must offer This however needs experiments over a large number of domains and applications on the way to identify rules generalize them reach a certain linguistic adequacy and predictability and elaborate a comprehensive set of linguistic marks etc Authoring tools would be useful for various kinds of operations including checking duplicates and overlaps among large sets of rules While some tools are available for sentence processing e g Sierra et al 2008 there is no such tool customized for discourse We develop in this section some considerations about a methodology for writing rules and what the services an authoring tool should offer such a tool is under investigation Some investigations have been realized to identify linguistic marks on subsets of discourse relations Rosner et al 1992 Redeker 1990 Marcu 1997 Takechi et al 2003 and Stede 2012 These mostly establish general principles and methods to extract terms characterizing these relations rules are then also written by hand i e rules do not result from automatic learning procedures The linguistic and pragmatic forms and principles that have emerged seem to be compatible with our per
18. bstantial knowledge contrary to an illustration Very informally the binding rule that binds and illustration with the illustrated texts pan can be defined as follows assuming here that these are all NPs with well identified types Illustrate lt illustrated gt NP Type lt illustrated gt gap G lt illustration gt NP1 Typel NP2 Type2 lt illustration gt subsume Type Typel subsume Type Type2 The subsumtion control makes sure that the Type of the illustrated is more general than the type of the elements in the illustration 2 13 Restatement Definition Relation where segment B rephrases segment A without adding further infor mation Restatement ponct opening parenthesis exp restate gap G ponct closing parenthesis exp restate gap G eos Resources exp restate in other words to put it another way that is to say i e put differently Examples If you must say something in a complicated way spanning several sentences try adding a sentence to summarize the idea In other words make every effort possible to be clear about each point in the essay When you revise your essay you need to ask yourself is this argument well made are there are any gaps in my argument am I making the case as precisely as I can are there are any premises or points that I make which aren t integrated into the whole paper In other words you ll continue to analyze your essay from the organiz
19. ccurences of the same structure in a sentence it is best to have a more complex rule that repeats the structure to find This is not so elegant but allows to limit backtracking and entails a much better efficiency It is also possible to suppress a in the code but this is not recommended 31 Bibliographie 1 Bal B K Saint Dizier P 2010 Towards Building Annotated Resources for Analyzing Opinions and Argumentation in News Editorial LREC Malta 2 Carberry S 1990 Plan Recognition in natural language dialogue Cambridge university Press MIT Press 3 Carlson L Marcu D Okurowski M E 2001 Building a Discourse Tagged Corpus in the Framework of Rhetorical Structure Theory In Proceedings of the 2nd SIGdial Workshop on Discourse and Dialog Aalborg 4 Colmerauer A 1978 Metamorphosis Grammars in Natural language understanding by computers L Bolc ed LNCS no 63 Springer verlag 5 Delin J Hartley A Paris C Scott D Vander Linden K 1994 Expressing Proce dural Relationships in Multilingual Instructions Proceedings of the Seventh International Workshop on Natural Language Generation pp 61 70 Maine USA 6 Di Eugenio B and Webber B L 1996 Pragmatic Overloading in Natural Language Ins tructions International Journal of Expert Systems 7 Fontan L Saint Dizier P 2008 Analyzing the explanation structure of procedural texts dealing with Advices and Warnings International S
20. cent nucleus may be linked to several satellites of different types some satellites may be embedded into their nucleus Selective binding rules allow the binding of 1 two adjacent structures in general a nucleus and a satellite or another nucleus 2 two or more non adjacent structures which may be separated by various elements e g causes and consequences conclusion and supports of arguments may be separated by va rious considerations However limits must be imposed on the textual distance between units To limit the textual distance between argument units we introduce the notion of bounding node which is also a notion used in sentence formal syntax to restrict the way long distance dependencies can be established Lasnik et al 1988 Bounding nodes are also defined in terms of barriers in generative syntax In our case the constraint is that a gap must not go over a bounding node This allows to restrict the distance between the constituents which are bound For example we consider that an argument conclusion and support must be both in the same paragraph therefore the node paragraph is a bounding node This declaration is taken into account by the lt TextCoop gt engine in a transparent way and interpreted as an active constraint which must be valid throughout the whole parsing process The situation is however more complex than in sentence syntax Indeed bounding nodes in discourse depend on the structure be
21. d up to the rule author The computation of the representation is typical of logic based grammars and use the power of logic variables of logic programming 3 R is a sequence of symbols as described below and 4 P is a set of predicates and functions implemented in Prolog that realize the various computations and controls and that allow the inclusion of inference and knowledge into rules R is a finite sequence of the following elements terminal symbols that represent words expressions punctuations various existing html or XML tags They are included between square brackets preterminal symbols are symbols which are derived directly into terminal elements These are used to capture various forms of generalizations facilitating rule authoring and update Symbols can be associated with a type feature structure that encodes a variety of aspects of those symbols from morphology to semantics non terminal symbols which can also be associated with type feature structures These symbols refer to local grammars i e grammars that encode specific syntactic construc tions such as temporal expressions or domain specific constructs Non terminal symbols do not include discourse structure symbols Dislog rules cannot call each other this feature being dealt with by the selective binding principle which includes additional controls A rule in Dislog thus basically encodes the recognition of a discourse function taken in isolatio
22. e where the enumeration itself is subject to dislocation 1 3 The lt TextCoop gt engine Let us now give some details about the way the lt TextCoop gt engine runs The engine and its environment are implemented in SWI Prolog using the standard Prolog syntax without using any libraries to guarantee readability ease of update and portability Since this is quite a 11 complex implementation we simply survey here the elements which are crucial for our current purpose The principle is that the declarative character of constraints and structure processing and building is preserved in the system The engine implemented in Prolog interprets them at the appropriate control points The constraints advocated above remain as given in the examples below these are directly consulted by the meta interpreter to realize the relevant controls The engine follows the cas cade specification for the execution of rule clusters For each cluster rules are activated in their reading order one after the other Backtracking manages rule failures If a rule in a rule cluster succeeds on a given text span then the other possibilities for that cluster are not considered but rules of other clusters may be considered in a later stage of the cascade A priori the text is processed via a left to right strategy In a cluster of rules rules are execu ted sequentially however if a rule starts with an early symbol e g a determiner it is activated before anot
23. e as specific as possible of the construct Each rule should imple ment a particular form of a discourse function In general the number of rules for a discourse function which form a cluster of rules ranges from 5 to about 25 rules About 10 are really ge neric while the others relate much more restricted situations This means that managing such a set of rules and evaluating them for a given function on a test corpus is feasible The next step is to order rules in the cluster starting by the most constrained ones consi dering the processing strategy implemented in lt TextCoop gt In general the most constrained rules correspond to less frequent constructions than generic ones which could be viewed as the by default ones In this case this means going through a number of rules with little chances of success involving useless computations As an alternative it is possible to start by generic 21 rules if 1 they correspond to frequently encountered structures and 2 they start by specific symbols not present in the beginning of other rules In this case backtracking would occur immediately This is a compromise frequently encountered in Logic programming that needs to be evaluated by the rule author Overlap of new rules with already existing ones must be investigated since this will ge nerate ambiguities This is essentially a syntactic task that requires rule contents inspection This task could certainly be automated in an authori
24. e complexity symbols per rule Mb of text h 0 0 1 o W Fig 3 Impact of rule size With the number of rules and the size of the lexicon being kept fixed we note that the rule size has a moderate impact on performances slightly higher than the number of rules This may be explained by the fact that the symbols starting the rules are in a number of cases sufficiently well differentiated to provoke early backtracking if the rule is not the one that must be selected However the number of lexical entries of these symbols may have an important impact If the symbol is a specific type of connector or if it is a noun or a verb this may entail efficiency differences difficult however to evaluate at our level Finally note that rules have in general between 4 and 6 symbols including gaps 1 3 2 The lt TextCoop gt environment The lt TextCoop gt environment is in a very early stage of development many more ex periments are needed before reaching a stable analysis of the needs It includes tools for rules syntax checking but also e g controlling possible overlaps between rules bootstrapping on corpora to induce rules and for developing the necessary lexical resources Accessing already defined and formatted resources is of much interest for authors We have already designed the following sets of resources for French and English These are not fully included in this first version but will be given in a second release Resources are t
25. ee os 2 8 COMMAS lie ts a e a dk VP hh nee eh ae Ge 29 Circumstance cas ile ees ed hae ee any Cc V RD Av ae ee Bde oe DAD PULPOSE a O IS E e REDE ice Baca R gee es 2 11 Husta soma ete AN A na Kaw oe Ba ae a 2 12 Illustration a simple example of a reasoning schema for binding purposes 2 13 Rest tement cidad Bae ek Bie A beg ol et baie o 2 14 The Art of writing Dislog rules and constraints Using Dislog and TextCoop 3 1 A few warnings before starting o o e e 3 2 Installation ok eR a le ls e EE e RA DO are Ee 313 A ot aaa od Sus LE C ES ha ck gd amp 34 Writing rules it e ago de ncia SE ee de ak oe e 3 4 1 3 4 2 3 4 3 3 4 4 3 4 5 3 4 6 3 5 Execution schema and structure of control Writing a rule in Dislog o oo e 25 Writing context dependent rules o o 27 Parameters and Structure Declarations 27 Lexical datas it Secar dl Moe ot Mo ee a PA ea Bes 28 Other TESQUICES i ces a e Se eb ee a et eA an 29 Input Output management 00 30 Warning This document is a preliminary version of a user manual for the lt TextCoop gt platform and the Dislog language The code which is distributed is also a kind of pre beta version It has however been tested without any problem on a number of types of texts with a large diversity of rules The software which is delivered at the moment is
26. en encoding this small set in Dislog is much faster checking for needed lexical resources writing the rules checking overlaps and testing the system on a toy text should not take more than a day or two for a somewhat trained person Our current environment contains about 280 rules describing 16 discourse structures associated with argumentation and explanation These rules are essentially the core rules for these 16 discourse structures 1t is clear that they can be used as a kernel for developing variants or more specific rules for these structures or for structures that share some similarities in form This should greatly facilitates the development of new rules for trained authors as well as for new ones Coming back to an authoring tool it is necessary at a certain stage to have a clear policy to develop the lexical architecture associated with the rule system Redundancies e g developing marks for each function even if functions share a lot of them should be eliminated as much as possible via a higher level of lexical description This would also help update reusability and extensions 22 Chapitre 3 Using Dislog and TextCoop 3 1 A few warnings before starting Please consider the following points before starting itis preferable to have some basic knowledge of Prolog before starting to use lt TextCoop gt we ask you that you do not modify the kernel of the system we are not responsible of any consequences that may ar
27. ency management The current lt TextCoop gt engine is close to the Prolog execution schema However to properly manage rule execution but also the properties of discourse structures and the way they are usually organized we introduce additional constraints which are for most of them borrowed from sentence syntax Within a cluster of rules the execution order is the rule reading order from the first to the last one Then elementary discourse functions are first identified and then bound to others to form larger units via selective binding rules Following the principle that a text unit has one and only one discourse function but may be bound to several other structures via several rhetorical relations and because rules can be ambiguous from one cluster to another the order in which rule clusters are executed is a crucial parameter There is indeed no backtracking over previous elements in the cluster to revise a parse that has succeeded In our engine there is no backtracking between clusters To handle this strategy Dislog requires that rule clusters are executed in a precise predefined order implemented in a cascade of clusters of rules For example if in a procedure we want first titles then prerequisites and then instructions to be identified the following constraint must be specified title lt prerequisite lt instruction Since titles have almost the same structure than instructions but with additional features bold fo
28. good essay on English literature you need to do five things In order to make the best of a writing assignment there are a few rules that can always be followed 2 11 Illustration Definition Relation where segment B instantiates a member of segment A used a repre sentative sample for the class represented by segment A Illustration gt exp illus eg gap G eos here auxiliary be gap G1 exp illus exa gap G2 eos let us take gap G exp illus bwe eos Resources exp illus_eg e g including such as exp illus_exa example an example examples exp illus_bwe by way of example by way of illustration 19 Examples This is a crucial point for other types of writing such as fiction or personal essay writing Here are some examples of how they can be used well so long as they are relevant to the essay 2 12 Illustration a simple example of a reasoning schema for bin ding purposes It is often difficult to identify exactly the text span which is illustrated As introduced in Chapter 1 in an expression such as red fruit tart strawberries raspberries etc the illus tration strawberries raspberries etc must be properly bound to red fruit only Besides the fact that the two structures illustration and illustrated are not adjacent this relation holds only if it is know that the fruits mentioned are red fruits If not the relation is rather an elaboration which adds su
29. he following lists of connectors organized by general types temporal causal concession etc list of specific terms which can appear in a number of discourse functions e g terms specific of illustration summarization reformulation etc lists of verbs organized by semantic classes close to those found in WordNet that we have adapted or refined for discourse analysis e g with a focus e g on propositional attitude verbs report verbs Wierzbicka 1987 etc list of terms with positive or negative polarity essentially adjectives but also some nouns and verbs this is useful in particular to evaluate the strength of arguments local grammars for e g temporal expressions expression of quantity etc some already defined modules of discourse function rules to recognize general purpose discourse functions such as illustration definition reformulation goal and condition some predefined functions and predicates to access knowledge and control features e g subsumption morphosyntactic tagging functions some basic utilities for integrating knowledge e g ontologies into the environment 14 Chapitre 2 Writing Dislog rules In this chapter we present some examples of Dislog rules which can recognize basic dis course structures such as instruction illustration reformulation etc We also give examples of binding rules Examples remain simple possibly adhoc they are given as
30. her rule that starts on a later symbol e g the noun it quantifies lt TextCoop gt also offers a right to left strategy for rules where the most relevant markers are to the right of the rule in order to limit backtracking For the two types of readings the system is tuned to reco gnize the smallest text span that satisfies the rule structure It processes raw text html or XML texts A priori the initial XML structure is preserved 1 3 1 System performances and discussion Let us now analyze the performances of lt TextCoop gt with respect to relevant linguistic dimensions General results The lt TextCoop gt engine and related data are implemented in SWI Prolog which runs on a number on environments Windows Linux Apple Our implementation can support a multi threaded approach which has been tested with the lt TextCoop gt engine embedded into a Java environment This is also useful for example for parallel processing on several machines or to distribute e g lexical data grammars and domain knowledge on various machines The lt TextCoop gt engine has been relatively optimized and some recommendations for writing rules have been produced in order to allow for a reasonable efficiency Lexical issues An important feature of discourse structure recognition is that the lexical resources which are needed are quite often generic This means that the system can be deployed on any applica tion domain without any major lexical cha
31. ing processed For example in the case of procedural discourse a warning can be bound to one or more instructions which are in the same subgoal structure Therefore the bounding node must be the subgoal node which may be much larger than a paragraph Bounding nodes are declared as follows in Dislog boundingNode paragraph argument Repair rules Although relatively unusual when parsing or computing representations annotation errors may occur This is in particular the case when 1 a rule has a fuzzy or ambiguous ending condi tion w r t the text being processed or 2 when rules of different discourse functions overlap leading to closing tags that may not be correctly inserted In argument recognition we have indeed some forms of competition between a conclusion and its support which share common linguistic markers For example when there are several causal connectors in a sentence the beginning of a support is ambiguous since most supports are introduced by a connector In addition to using concurrent processing strategies repair rules can resolve errors efficiently The most frequent situation is the following lt a gt lt b gt lt a gt lt b gt which must be rewritten into lason S fares bara L DS This is realized by the following rule correction lt A gt Gl lt A gt G2 lt B gt G3 lt B gt gt lt A gt gap G1 lt B gt gap G2 lt A gt gap G3 lt B gt Rule concurr
32. ise if you do so a priori Prolog does not recognize some characters encoded in UTF8 but only in the ISO formats some conversions manual or via a programme are needed if you have UTF 8 encoded texts only the kernel of the system is given here with a few rule samples There is no interface provided although this would be desirable You can however design you own depending on what you want to do and see 3 2 Installation lt TextCoop gt is implemented in Prolog only the kernel of Prolog is used so most versions of Prolog using the Edinburgh syntax should work We recommend to use SWI Prolog which is free and runs efficiently on several platforms Note that it runs faster on a Linux environment than in Windows Then the only thing you have to do is to unzip the lt TextCoop gt archive into a directory of your choice A priori it is simpler to keep all the files in a single directory However the text files you process can be in a subdirectory Basically the archive contains two directories one for the French version and the other one for the English version files end by Fr or Eng depending on the language Each directory contains the following files the engine textcoopV4 pl a specific file for user declarations and parameters decl pl aset of functions fonctions pl aset of lexicons of various terms lexiqueBase pl lexSpecialise pl lexiquelllustr pl you can obviously construct several additi
33. n almost 200 relations have been introduced with various aims http www sfu ca rst Several approaches based on corpus analysis with a strong linguistic basis are of much interest for our aims Relations are investigated together with their linguistic markers e g Delin 1994 Marcu 1997 Miltasaki et ali 2004 then Kosseim et al 2000 for language generation and Rossner et al 1992 and Saito et al 2006 with an extensive study on how markers can be quite systematically acquired TextCoop is a logic based platform designed to describe and implement discourse struc tures and related constraints via an authoring tool Dislog Discourse in Logic is the language designed for writing rules and lexical data Dislog extends the formalism of Definite Clause Grammars and Metamorphosis Grammars since type 1 expressions are allowed to discourse This first chapter is a revision of our LREC 2012 paper on TextCoop foundations processing and allows the integration of knowledge and inferences TextCoop and Dislog tackle the following foundational and engineering problems taking into account of the diversity of discourse structures generic e g illustration elaboration as well as domain oriented e g title instructions in procedures introduction for easy tests and updates of a declarative and modular language via rules Our approach is based on 1 basic discourse structures 2 selective binding rules to bind basic struct
34. n i e an elementary discourse unit optionality and iterativity markers over non terminal and preterminal symbols as in re gular expressions gaps which are symbols that stand for a finite sequence of words of no present interest for the rule which must be skipped A gap can appear only between terminal preterminal or non terminal symbols Dislog offers the possibility to specify in a gap a list of ele ments which must not be skipped when such an element is found before the termination of the gap then the gap fails afew meta predicates to facilitate rule authoring Symbols may have any number of arguments However in our current version to facilitate the implementation of the meta interpreter and improve its efficiency the recommended form is identifier Representation Type feature structure where Representation is the symbols representation In Prolog format a difference list E S is added at the end of the symbol identifier R TFS E S A few examples in Dislog format are given in Chapter 3 Rules in external format illustrating the above definitions can be found in Chapter 2 Similarly to DCGs and to Prolog clause systems it is possible and often necessary to have several rules to describe the different realizations of a given discourse function These all have the same identifier L as it would be the case e g for NPs or PPs A set of rules with the same identifier is called a cluster of rules Rule cluste
35. n as parameter It has the same structure as a gap except that the second argument is an integer in general small skip NotSkipped Number E S Skipped The symbol in the rule that immediately follows the skip symbols defines the termination of the skip The rule given above then translates as follows in Dislog forme purpose eng E S connecteur CONN goal E E1 verb V action impinf E1 E2 gap ponct _ E2 E3 Sautel ponct Ponct _ E3 S l lt purpose gt CONN V Sautel lt purpose gt Ponct It is given as a Prolog fact so that the TextCoop meta interpretor can read it The reader can note the sequences of input output variables E1 E2 E3 S as in DCGs The last argument encodes the resul for example the way the original sentence is tagged Tags may be inserted at any place they may contain variables elaborated in the inference also called Constraints field In fact any form of representation can be produced in this field Symbols in a rule car be marked as optional or can appear several times including none This is encoded via the operators opt and plus applied to the grammar symbols forme purpose eng E S connecteur CONN goal E E1l opt verb V action impinf E1 E2 gap ponct _ E2 E3 Sautel ponct Ponct _ E3 S 26 1 lt purpose gt CONN V Sautel lt purpose gt Ponc In
36. ndow or in the window by typing textcoopv4 pl care about ERROR messages but you can ignore warnings It is recommended that the texts you want to analyze are in txt format Care to have only ISO character encodings otherwise Prolog will create huge files via a loop Character encoding may be tuned in some environments Then to launch TextCoop type the main call annotF then you are required to enter your file name between quotes ended by a dot as usual in prolog demo txt You will then see a large number of intermediate files which are created and re used Each cycle corresponds to the execution of a cluster of rules in the rule cascade Results are stored in two files demo out html html tags no color and demo c out html same thing but with colors and spaces to facilitate reading The file es p1 contains a few other input output calls that you may wish to explore You can also change the display colors in this file or add or withdraw the display of some tags The contents of the tags is produced by discourse analysis rules described in the Representation argument 3 4 Writing rules Rules are stored in the present archive in the rules p1 file The rules can be produced from a manual analysis of linguistic phenomena or be the result of a statistical analysis A priori the Dislog language is flexible enough to accept a large variety of forms Rule definition 24 methodology is presented in Cha
37. ng tool Ambiguities may be resolved by using knowledge If it turns out that this is not possible then preferences must be stated a certain function must be preferred to another one Preferences can then be coded in the cas cade starting with the preferred rule clusters the recognition of the competing rules being then excluded The last stage for rule writing is the development of selective binding rules and possibly correction rules for anomalous situations Selective binding rules are relatively easy to produce since they are based on the binding of two already identified structures Structure variabi lity long distance or dislocations are automatically managed by the lt TextCoop gt engine in a transparent way Finally the rule writer must add the cluster name at the right place in the cascade and possibly state constraints as given in Chapter 1 Although there are important variations the total investigations for encoding from scratch a discourse function of a standard complexity including corpus collection readings and testing should take a maximum of about one month full time This is a very reasonable amount of time considering e g the workload devoted to corpus annotation in the case of a machine learning approach We feel the quality of manual encoding is also better in particular rule authors are aware of the potential weaknesses of their descriptions If a rule or a small set of rules are already available in an informal way th
38. nges and update see chapter 2 In total the average size of the required lexical resources number of rules being fixed for discourse processing for an application such as procedural text parsing on a given domain is around 900 words which is very small compared to what is necessary to process the structure of sentences for the same domain Results below are given fro French Results for English are not very different The following figures give the system performances depending on the lexicon size Lexicon sizes correspond to comprehensive lexicons for a given domain e g 400 corresponds to the cooking domain the case with 180 lexical entries is a toy system 12 lexicon size in no of words Mb of text hour 180 3 400 Nj N 1400 2000 Fig 1 Impact of lexicon size 18 1 i These results are somewhat difficult to precisely analyze since they depend on the number of words by syntactic category the way they are coded and the order in which they are listed in the lexicon in relation with the Prolog strategy In order to limit the complexity related to morphological analysis a crucial aspect for Romance languages a preliminary tagging process has been carried out to limit backtracking The way lexical resources are used in rules is also a parameter which is difficult to precisely analyse Globally reducing the size of the lexicon to those elements which are really needed for the application allows for a certain increase in
39. nomenon e g purpose satellites It will be used in the cascade to refer to this cluster and in various constraints E and S and respectiveley the input and output strings of the sentence or text to process similarly to DCGs The informal meaning is that between E and S there is a purpose clause E and S are lists of words as in Prolog RuleBody is the right hand part of the rule which is described below Constraints is a list that contains a variety of constraints to check or calls to knowledge and reasoning procedures these are in general written in Prolog and are executed au tomatically at the end of the rule analysis An empty list means no constraints and is always evaluated to true Result denotes the result which includes the string of words of the input structure with tags included at the appropriate places Tags in rules may include variables The rule body is encoded as follows First each grammar symbol has four arguments this is a choice which can be modified but seems optimal and easy to use Each symbol has the following form name String Feature E S where String denotes the String which is restored in the result In general it is the string that has been read for that symbol e g E S but it can be any other form e g a normalized form a reordered string etc Feature is the argument that contains a list of restrictions encoded e g as a list of values 25 or as a type feature struct
40. nt html specific tags etc this prevents titles from being erroneously identified as instruc tions In relation with this notion of cascade it is possible to declare closed zones e g closed_zone title indicates that the textual span recognized as a title must not be considered again to recognize other functions within or over it via a gap Structural constraints Let us now consider basic structural principles which are very common in language syntax This allows us to contrast the notion of consistuency with the notion of discourse relation Consistuency is basically a part of relation applied to language structures nouns are part of NPs while discourse is basically relational Let us introduce here dominance and precedence constraints these notions being valid as far as trees of discourse structures can be constructed which is in fact the most frequent situation Discourse abound in various types of constraints which may be domain dependent Dislog is open to the specification of a number of such structural constraints These are interpreted by the meta interpreto in lt TextCoop gt as active constraints Dominance constraints can be stated as follows dom instruction condition This constraint where instruction and condition are two cluster names states that a conditio nal expression is always dominated by an instruction i e the condition XML tags are strictly within the boundaries of an instruction XML
41. ntroduction of reasoning and the lt TextCoop gt plat form allows the integration of knowledge and functions to access it and reason about it This problem is very vast and largely open with exploratory studies e g reported in Van Dijk 1980 Kintsch 1988 and more recently some debates reported in http www discourses org Unpublished Articles SpecDis amp Know htm 10 Let us give here a simple motivational example The utterance found in our corpus red fruit tart strawberries raspberries are made contains a structure strawberries raspberries which is ambiguous in terms of discourse functions it can be an elaboration or an illustration furthermore the identification of its nu cleus is ambiguous red fruit tart red fruit A straightforward access to an ontology of fruits tells us that those berries are red fruits there fore the unit strawberries raspberries is interpreted as an illustration since no new information is given otherwise it would have been an elaboration its nucleus is the red fruit unit only and it should be noted that these two constituents which must be bound are not adjacent The relation between an argument conclusion and its support may not necessarily straight forward and may involve various types of domain and common sense knowledge do not park your car at night near this bar it may cost you fortunes Women standards of living have progressed in Nepal we now
42. onal lexicons you must add their compilation at the beginning of the textcoopV4 pl file The French version also contains a list of 23 categorized verbs eeeaccents pl a file with rules or patterns rules pl a toy file with local grammar samples written in DCG format gram p1 a file for the input output operations es p1 and for reading files in Prolog Lire pl afew files to run the system on examples demo t xt after processing the system pro duces two output files demo out html1 tags no colors and demo c out html same thing but with colors and spaces to facilitate reading However note that we have not developed at this stage any user interface additional files can be added for example to include knowledge or specific reasoning procedures Nothing is included at this level in this pre beta version 3 3 Starting There are several ways to read and modify your files and to call Prolog Emacs and similar editors are particularly well adapted We recommend the use of Editplus V3 for those who do not have any preference Prolog can be launched directly from the editor and the code can be easily re interpreted when needed To start the system you must launch Prolog from you environment e g from Editplus You must then consult your file s Since the file text coopV4 p1 contains consult orders for all the other files you just need to consult it via the menu of the Prolog wi
43. pter 2 together with a number of examples to which the reader can refer The first thing as indicated in the previous chapter is to make a linguistic analysis gene ralize at the appropriate level develop the lexical resources cues typical of the relation and other resources and write rules in an external format as shown in Chapter 2 We show in this chapter how to write rules in Dislog and to manage clusters of rules the cascade and various types of constraints 3 4 1 Writing a rule in Dislog Let us first take an example The rule that describes a purpose satellite can be written in an external format as follows purpose connector goal verb action impinf gap G punctuation e g To write a good essay on English literature you need to do five things This rule says that a purpose satellite is composed of a connector of type goal followed by an action verb in the imperative or infinitive form followed by a gap The structure ends by a punctuation mark Labels such as goal impinf or action are defined by the rule author they are not imposed by the system These tags may be encoded in a variety of ways as lists as in this example or as a feature structure The general form of a rule coded in Dislog is forme LHS E S RuleBody Constraints Result where LHS is the symbol on the left hand side of the rule It is the name of the cluster of rules representing the various structures corresponding to a phe
44. ref infinitive gap G eos lit is adv prob gap G1 exp advicel gap G2 eos exp advice2 gap G eos Resources verb pref choose prefer exp advicel a good idea better recommended preferable exp advice2 a X tip a X advice best option alternative adv_prob probably possibly etc Examples Choose aspects or quotations that you can analyse successfully for the methods used ef fects created and purpose intended Following your thesis statement it is a good idea to add a little more detail that acts to preview each of the major points that you will cover in the body of the essay A useful tip is to open each paragraph with a topic sentence 2 3 Warning Definition Relation between a conclusion and a support the conclusion drawing the attention of the reader to an action which is compulsory to perform and the support giving a motivation for realizing this action or the risks which may arise Structures Warning conclusion exp ensure gap G eos lit is adv int adj imp gap G verb action infinitive gap G eos Resources 16 exp ensure ensure make sure be sure adv int very absolutely really adj imp essential vital crucial fundamental Examples Make sure your facts are relevant rather than related It is essential that you follow the guidelines for each proposal as set by the instructor 2 4 Binding rules for warnings Let us give here a simple example
45. rs are executed sequentially by the lt TextCoop gt engine following an order given in a cascade 1 2 4 Dislog advanced features Selective binding rules Selective binding rules allow to link two or more already identified discourse basic units The objective is e g to bind a nucleus with a satellite e g an argument conclusion with its support or with another nucleus e g a concessive or parallel structures Selective binding rules can be used for other purposes than implementing rhetorical relations These can be used more generally to bind structures whose rhetorical status is not so straightforward in particular in some application domains For example in procedural discourse they can be used to link a title with the set of instructions prerequisites and warnings that realize the goal expressed by this title From a syntactic point of view selective binding rules are expressed using the Dislog lan guage formalism Selective binding rules is the means offered by Dislog to construct hierar chical discourse structures from the elementary ones identified by the rule system Different situations occur that make binding rules more complex than any system of rules used for sen tence processing in particular examples are given in section 2 6 discourse structures may be embedded to a high degree with partial overlaps others may be chained a satellite is a nucleus for another relation nucleus and related satellites may be non adja
46. s is traversed from left to right this code also provides a right to left strategy at each step i e for each word the engine attempts to find a rule in that cluster that would start by this word via derivation or lexical inspection If it works then that rule independently of its position in the cluster is activated If the whole rule succeeds then no other rule is considered in that cluster otherwise backtracking occurs For example consider the following sentence to process a n a d f b c and the following simplified set of rules SA dE S gt a Du SE ad The first a and then the n are considered without any success but then the second occurence of a is the left corner of the second and third rules The second fails since no b is found after a but the third rule succeeds Note that in DCGs the first rule would have succeeded on a partial parse because the sentence contains the sequence d f but since it comes later than the sequence a d it does not succeed in Dislog This strategy favors left extraposed structures in case there are several of them in a sentence Note also that if the second rule where s a gap b then it would have succeeded first on the segment a n a d f b with gap n a d f In our approach when a rule in a cluster succeeds for efficiency reasons no other rule in that cluster is considered any more and there is no backtraking at any further stage For users who want to recognize several o
47. see long lines of young girls early morning with their school bags Nepali Times In this latter example school bag means going to school then school means education which in turn means better conditions for women in this case Predefined reasoning functiosn are not yet available however the rule writer can define his own 1 2 6 Processing complex constructions the case of Dislocation constructions As in any language situation there are complex situations where discourse segments that contribute to form larger units which are not clearly delimited may overlap be shared by se veral discourse relations etc Similarly to syntax we identified in relatively free style texts phenomena similar to quasi scrambling situations free structure ordering and cleft construc tions From a processing point of view the lt TextCoop gt engine attempts to recognize the em bedded structure first then if no unique text segment can be found for the embedding structure standard case it non deterministically decomposes the rules describing the embedding struc ture one after the other following the above constraints and attempts to recognize it around the embedded one As an example we observed in our corpora quasi scrambling situations a simple case being the illustration relation Consider again the example above which can also be written as follows in French strawberries are red fruits similarly to raspberries for exampl
48. spective At the present stage rules are basically written by hand Although this is not the main trend nowadays we feel this is the most reliable approach given the complexity and variability of discourse structures and the need to elaborate semantic representations Let us briefly review here how rules are produced The first step is given a discourse function one wants to investigate to produce a clear definition of what it exactly represents and what is its scope possibly in contrast with other functions This is realized via a first corpus construction where a number of realizations over several domains are collected analyzed and sorted by decreasing prototypicality order This must be realized preferably by a few people and in connection with the literature in order to reach the best consensus Then a larger corpus must be elaborated possibly via bootstrapping tools Morphosyntactic tagging contributes to identifying regularities and frequencies From this corpus a categorization must be first elaborated of the different lexical resources which are needed Then rules can be written Rules should be expressed at the right level of abstraction to account for a certain level of predictability and linguistic adequacy This means avoiding low level rules one rule per exceptional case or too high level rules which would be difficult to be constrained Rules must be well delimited starting and ending by non terminal or terminal symbols which ar
49. t be a collage of other scholars thoughts and research although you will definitely rely upon these scholars as you move toward your point 2 8 Contrast Definition Relation where one segment is opposed to another segment Contrast conn oppositionwhe gap G ponct comma conn oppositionwhe gap G eos conn opposition how gap G eos Resources conn opposition_whe whereas but whereas but while 18 Examples The periodic sentence is one in which the main clause is considerably delayed whereas the cumulative sentence opens quickly with the main clause 2 9 Circumstance Definition Relation where the segment B refers to a frame in which A is to be realized by the reader of the procedure Circumstance gt conn circ gap G ponct comma conn circ gap G eos Resources conn circ when once as soon as after before Examples Before you put your outline together you need to identify your argument and analyze it Once you use a piece of evidence be sure and write at least one or two sentences explaining why you use it 2 10 Purpose Definition Relation where segment B provides the aim targeted by the realization of the action expressed in segment A Purpose conn purpose verb action infinitive gap G ponct comma conn purpose verb action infinitive gap G eos Resources conn purpose fo in order to so as to Examples To write a
50. tags This means that a condition must always be part of an instruction not in a discourse relation with an instruction In that case there is no dis course link between a condition and an instruction the implicit structure being consistuency a condition is a constituent or a part of an instruction Similarly non dominance constraints can be stated to ensure that two discourse functions appear in different branches of the discourse representation e g not_dom instruction warning which states that an instruction cannot dominate a warning Finally precedence constraints may be stated We only consider here the case of imme diate linear precedence for example prec elaborated elaboration indicates that an elaboration must follow what is elaborated This is a useful constraint for the cases where a nucleus must necessarily precede its satellite it contributes to the efficiency of the selective binding mechanism and resolves some recognition ambiguities 1 2 5 Introducing reasoning aspects into discourse analysis Discourse relation identification often require some forms of knowledge and reasoning This is in particular the case to resolve ambiguities in relation identification when there are several candidates or to clearly identify the text span at stake While some situations are extre mely difficult to resolve others can be processed e g via lexical inference or reasoning over ontological knowledge Dislog allows the i
51. this example the verb is indicated as optional if it is not found then the gap starts after the goal marker If there is no verb the variable V in the result is not instantiated and does not produce any result forme purpose eng E S connecteur CONN goal E E1 plus aux Aux E1 E2 verb V action _ E2 E3 gap ponct _ E3 E4 Sautel ponct Ponct _ E4 S l lt purpose gt CONN Aux V Sautel lt purpose gt Ponc In this example a sequence of auxiliaries is allowed before the verb 3 4 2 Writing context dependent rules A closer look at the Dislog rule formalism shows that it is possible to use this formalism to implement context dependent rules In fact the left hand side symbol the cluster name can be just viewed as an identifier and the power of the rule can be shifted to the pair right hand part and representation The right hand part is indeed often the input form to identify and the Representation is the result allowing a large diversity of treatments Wr already advocated the case of binding rules which are clearly type 1 if not type 0 rules It is possble to develop any other kind of rules e g to realize structure transformation with some context sensitivity If we consider again the example given in Chapter 2 Warning lt warning concl gt gap G1 lt warning concl gt gap G2 lt warning supp gt gap G3 lt warning supp
52. ual lt TextCoop gt is the first platform that offers this view within a logic based approach If machine learning is a possible approach for sentence pro cessing where interesting results have emerged it seems not to be so successful for discourse analysis e g Carlson et ali 2001 Saaba et al 2008 This is due to two main factors 1 the difficulty to annotate discourse functions in texts and the high level of disagreement between annotators and 2 the large non determinism of discourse structure recognition where markers are often immerged in long spans of text of no or little interest For these reasons we adopted a rule based approach Rules are hand coded based on corpus analysis using bootstrapping tools They may also emerge from automatic learning procedures Dislog rules basically implement the productive principles They are composed of three main parts 1 A discourse function or unit identification structure which basically has the form of a rule or of a pattern 2 A set of calls to inferential forms using various types of knowledge these forms are part of the identification structure they may contribute to solving ambiguities they may also be involved in the computation of the resulting representation or they may lead to restrictions 3 A structure that represents the result of the analysis it can be a simple XML structure or any other structure a priori such as an element of a graph or a dependency structure
53. ucleus or a satellite of a rhetorical relation or si milar structure e g an illustration an illustrated expression an elaboration or the elaborated expression a conditional expression a goal expression etc Functions are realized by textual structures which need to be accurately delimited Functions are not stand alone they must be bound based on the nucleus satellite or nucleus nucleus principle 1 2 2 Some foundational principles of lt TextCoop gt The necessity of a modular approach open to the diversity of language constructs where each aspect of discourse analysis is dealt with accurately and independently in a module while keeping open all the possibilities of interaction if not concurrency between modules has lead us to consider some simple elements of the model of Generative Syntax a good synthesis is given in Lasnik et al 1988 As shall be seen below we introduce productive principles which have a high level of abstraction which are linguistically sound but which may be too powerful restrictive principles which limit the power of the first in particular on the basis of well formedness constraints Another foundational feature is an integrated view of markers used to identify discourse functions merging lexical objects with morphological functions typography and punctuation syntactic constructs semantic features and inferential patterns that capture various forms of knowledge domain lexical text
54. ure The format is here free but the rule writer must manage it Eand S are respectively the input and output lists of words as above for the analysis of this particular symbol Gap symbols have a different format gap NotSkipped Stop E S Skipped where NotSkipped is a list of symbols which must not be skipped before the gap stops deve loped in the second argument If it finds such a symbol then the gap fails So far this list is limited to a single symbol for efficiency reasons We haven t also found so many cases where multiple restrictions are needed If really needed these must be coded an example is provided in the TextCoop engine code where gaps are coded in the gap clause in the engine Stop is a list Symbol Restrictions that describes where the gap must stop it must stop immediately before it finds a symbol Symbol with the restrictions Restrictions In general this is the explicit symbol that follows but this is not compulsory Eand S are as above Skipped is the difference between E and S namely the sequence of words that have been skipped It must be noted that a gap must appear in a rule between two explicit symbols While proces sing a rule if a gap reaches the end of a sentence or a predefined ending mark without having found the symbols that follows then it fails The symbol skip is slightly different from the gap symbol It allows to skip a maximum mumber of N words give
55. ures into larger units 3 repair rules 4 various classes of constraints on the way basic structures can be combined and 5 reasoning procedures introduction of accurate specifications of rule execution modes e g order concurrency left to right or right to left etc in order to optimally process structures taking into account of the specification and binding of complex structures e g multi nucleus satellite constructions as often found in domain dependent constructions e g title prerequisites instructions in procedures or cases where satellites are merged into their nucleus dislocation integration in rules of various forms of knowledge and inferences e g to compute at tribute values or to resolve relation identification and scope or ambiguities between various relations development of an authoring tool to implement discourse relation rules and lexical re sources Note that in general discourse analysis rules are relatively re usable over do mains because markers are often domain independent finally production of various forms of output representations XML tags dependencies 1 2 The lt TextCoop gt platform and the Dislog language 1 2 1 Some linguistic considerations Most works dedicated to discourse analysis have to deal with the triad discourse func tion identification delimitation of its textual structure boundaries of the discourse unit and structure binding By function we mean a n
56. ymposium on Text Semantics STEP 2008 Venise Johan Bos Eds 8 Grosz B Sidner C 1986 Attention intention and the structure of discourse Computa tional Linguistics 12 3 9 Kintsch W 1988 The Role of Knowledge in Discourse Comprehension A Construction Integration Model Psychological Review vol 95 2 10 Kosseim L Lapalme G 2000 Choosing Rhetorical Structures to Plan Instructional Texts Computational Intelligence Blackwell Boston 11 Lasnik H Uriagereka J 1988 A Course in GB syntax MIT Press 12 Mann W Thompson S 1988 Rhetorical Structure Theory Towards a Functional Theory of Text Organisation TEXT 8 3 pp 243 281 13 Mann W Thompson S A eds 1992 Discourse Description diverse linguistic ana lyses of a fund raising text John Benjamins 14 Marcu D 1997 The Rhetorical Parsing of Natural Language Texts ACL 97 15 Marcu D 2000 The Theory and Practice of Discourse Parsing and Summarization MIT Press 32 16 Marcu D 2002 An unsupervised approach to recognizing Discourse relations ACL 02 17 Miltasaki E Prasad R Joshi A Webber B 2004 Annotating Discourse Connectives and Their Arguments proceedings of new frontiers in NLP 18 Pereira F 1981 Extraposition Grammars Computational Linguistics vol 9 4 19 Pereira F Warren D 1980 Definite Clause Grammars for Language Analysis Artificial Intelligence vol 13 3
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