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1. Multiword definition file See section 3 10 for details e Number Detection Command line Configuration file numb nonumb NumbersDetection yeslylon no nloff Whether to perform nummerical expression detection Deactivating this feature will affect the behaviour of date time and ratio currency detection modules e Decimal Point Command line Configuration file dec lt string gt DecimalPoint lt string gt Specify decimal point character for the number detection module for instance in English is a dot but in Spanish is a comma e Thousand Point Command line Configuration file thou lt string gt ThousandPoint lt string gt Specify thousand point character for the number detection module for instance in English is a comma but in Spanish is a dot 76 CHAPTER 5 USING THE SAMPLE MAIN PROGRAM TO PROCESS CORPORA Punctuation Detection Command line Configuration file punt nopunt PunctuationDetection yes y on nol n off Whether to assign PoS tag to punctuation signs Punctuation Detection File Command line Configuration file F lt filename gt fpunct lt filename gt PunctuationFile lt filename gt Punctuation symbols file See section 3 6 for details Date Detection Command line Configuration file date nodate DatesDetection yes y on nol n off Whether to perform date an
2. Prints the version number of currently installed FreeLing library e Configuration file Command line Configuration file f lt filename gt N A Specify configuration file to use default analyzer cfg e Server mode Command line Configuration file server ServerMode yeslylonlnoln off Activate server mode Requires that option port is also provided Default value is off e Server Port Number Command line Configuration file p lt int gt port lt int gt ServerPort lt int gt Specify port where server will be listening for requests This option must be specified if server mode is active and it is ignored if server mode is off e Maximum Number of Server Workers Command line Configuration file w lt int gt workers lt int gt ServerMaxWorkers lt int gt Specify maximum number of active workers that the server will launch Each worker attends a client so this is the maximum number of clients that are simultaneously attended This option is ignored if server mode is off Default vaule is 5 Note that a high number of simultaneous workers will result in forking that many processes which may overload the CPU and memory of your machine resulting in a system collapse When the maximum number of workers is reached new incoming requests are queued until a worker finishes e Maximum Size of Server Queue Command line Configuration
3. o o o e 3 18 Phonetic Encoding Module e 3 19 Named Entity Classification Module o o o e e 3 20 Chart Parser Moduler m kasu aa it GS Bo ad e 3 21 Dependency Parser Module e 3 22 Coreference Resolution Module o e e 3 23 Semantic Database Module 3 24 Feature Extraction Module e 4 Using the library from your own application dk Basic Classes ui a A aed A ee ee R AE RR N Mis We ey ee 42 Sample pr prio e a r a E RR de ee Se A YNN aA Kad kad GO CO G G al ii CONTENTS 5 Using the sample main program to process corpora 67 5 1 The easy way Using the analyze script ooo e e 67 5 2 Using a threaded analyzer o RE E RR R T 69 pide Usage example vibe do AR a a eE A e a a a a 69 5 4 Configuration File and Command Line Options 70 6 Extending the library with analyzers for new languages 83 Gel Tokenizer s o os e dd a 83 6 2 Morphological analyzer o e 83 63 HMM Pos Tappei i p d a n a e a A a AA A E E de S 85 6 4 Relaxation Labelling PoS Tagger oaaae 85 6 5 Named Entity Recognizer and Classifier ooo 85 6 6 Chart Parser ma SE a A a aan L K R te et i a 86 6 7 Dependency Parser 2 00 o E a a TR ee 86 Chapter 1 Introduction The FreeLing package consists of a library providing lang
4. a print analysis info wcout lt lt L _ lt ANALYSIS lemma Y lt lt a gt get_lemma wcout lt lt L pos lt lt a gt get_tag wcout lt lt L _prob Y lt lt a gt get prob wcout lt lt L X gt lt lt endl T close word XML tag after list of analysis wcout lt lt L lt WORD gt lt lt endl T close sentence XML tag wcout lt lt L lt SENT gt lt lt endl T The above sample program may be found in src main simple_examples sample cc in FreeLing tarball The actual program also outputs tree structures resulting from parsing which is ommitted here for simplicity Once you have compiled and installed FreeLing you can build this sample program or any other you may want to write with the command gt o sample sample cc lfreeling Option lfreeling links with libfreeling library which is the final result of the FreeLing compilation process Check the README file in the directory to learn more about compiling and using the sample programs You may need to add some I and or L options to the compilation command depending on where the headers and code of required libraries are located For instance if you installed some of the libraries in usr local mylib instead of the default place usr local you ll have to add the options I usr local mylib include L usr local mylib lib to the command above 4 2 SAMPLE PROGRAMS 65 Issuin
5. fill a vector with sorted probabilities for each language void rank_languages std vector lt std pair lt double std wstring gt gt amp const std wstring amp const std set lt std wstring gt amp const 10 CHAPTER 3 ANALYSIS MODULES Once created the language identifier may be used to get the most likely language of a text identify_language or to return a sorted vector of probabilities for each language rank_languages In both cases a set of languages to be considered may be supplied telling the identifier to apply to the input text only models for those languages in the list An empty list is interpreted as use all available language models The same lang_ident class may be used to train models for new languages The method train_language will use a plain text file to create a new model which will enlarge the identifier s language repertoire and will be stored for its use in future instances of the class The constructor expects a configuration file name containing information about where are the language models located and some parameters The contents of that file are described below 3 1 1 Language Identifier Options File The language identifier options file is divided in three sections lt Languages gt lt Threshold gt and lt ScaleFactor gt which are closed by lt Languages gt lt Threshold gt and lt ScaleFactor gt respec tively Section lt Languages gt contains a list of filenames
6. CMP03 Fel98 KVHA95 Pad98 SNLO1 Vos98 Jordi Atserias Elisabet Comelles and Aingeru Mayor Txala un analizador libre de dependencias para el castellano Procesamiento del Lenguaje Natural 35 455 456 September 2005 Eneko Agirre and Aitor Soroa Personalizing pagerank for word sense disambiguation In Proceedings of the 12th conference of the European chapter of the Association for Computational Linguistics EACL 2009 Athens Greece 2009 Thorsten Brants Tnt A statistical part of speech tagger In Proceedings of the 6th Conference on Applied Natural Language Processing ANLP ACL 2000 Chih Chung Chang and Chih Jen Lin LIBSVM A library for support vector ma chines ACM Transactions on Intelligent Systems and Technology 2 27 1 27 27 2011 Software available at http www csie ntu edu tw cjlin libsvm Xavier Carreras Llu s M rquez and Llu s Padr A simple named entity extractor using adaboost In Proceedings of CoNLL 2003 Shared Task Edmonton Canada June 2003 Christiane Fellbaum editor WordNet An Electronic Lexical Database Language Speech and Communication The MIT Press 1998 F Karlsson Atro Voutilainen J Heikkila and A Anttila editors Constraint Gram mar A Language Independent System for Parsing Unrestricted Text Mouton de Gruyter Berlin and New York 1995 Llu s Padr A Hybrid Environment for Syntax Semantic Tagging PhD thesis Dept Llenguatges i Sistem
7. The lt Abbreviations gt section defines common abbreviations one per line that must not be separated of their following dot e g etc mrs They must be lowercased even if they are expected to appear uppercased in the text 3 3 Splitter Module The splitter module receives lists of word objects either produced by the tokenizer or by any other means in the calling application and buffers them until a sentence boundary is detected Then a list of sentence objects is returned 12 CHAPTER 3 ANALYSIS MODULES The buffer of the splitter may retain part of the tokens if the given list didn t end with a clear sentence boundary The caller application can sumbit further token lists to be added or request the splitter to flush the buffer The API for the splitter class is class splitter public Constructor Receives a file with the desired options splitter const std string amp Add list of words to the buffer and return complete sentences that can be build The boolean states if a buffer flush has to be forced true or some words may remain in the buffer false if the splitter wants to wait to see what is coming next std list lt sentence gt split const std list lt word gt amp bool 3 3 1 Splitter Options File The splitter options file contains four sections lt General gt lt Markers gt lt SentenceEnd gt and lt SentenceStart gt The lt General gt section contains gener
8. Note that this module sequentially checks each regular expression in the list against each word in the text Thus it should be used for patterns not for fixed strings which can be included in a dictionary file and with moderation using a very long list of expressions may severely slow down your analysis chain 3 7 1 User Map File The format of the file containing the user map from regular expression to pairs lemma PoS is one regular expression per line each line with the format regex lemmal tagl lemma2 tag2 The lemma may be any string literal or meaning that the string matching the regular expression is to be used as a lemma E g a z 0 9 NPO0000 lt gt XMLTAG Fz hulabee hulaboo JJS hulaboo NNS 3 8 DATES DETECTION MODULE 17 The first rule will recognize tokens such as john or peter4 and assign them the tag NPO0000 proper noun and the matching string as lemma The second rule will recognize tokens starting with lt and ending with gt such as lt HTML gt or lt br gt and assign them the literal XMLTAG as lemma and the tag Fz punctuation others as Pos The third rule will assign the two pairs lemma tag to each occurrence of the word hulabee This is just an example and if you want to add a word to your dictionary the dictionary module is the right place 3 8 Dates Detection Module The dates detection module as the number detection module in section 3 5 is a collectio
9. The API of the class is the following class ukb_wrap 1 public Constructor Receives a relation file for UKB a sense dictionary and two UKB parameters epsilon and max iteration number ukb_wrap const std string 4 analyze given sentence void analyze sentence 4 analyze given sentences void analyze std list lt sentence gt amp return analyzed copy of given sentence sentence analyze const sentence amp return analyzed copy of given sentences std list lt sentence gt analyze const std list lt sentence gt amp 3 The constructor receives a file name where module configuration options are found The contents of the configuration files are the following e A section lt WNPosMap gt which establishes which PoS found in the morphological dictionary should be mapped to each WN part of speech These rules are described in section 3 23 e A section lt StopParameters gt specifying values for UKB stopping criteria E g lt StopParameters gt Epsilon 0 03 MaxIterations 10 lt StopParameters gt These two parameters are UKB parameters The an epsilon float value that controls the precision with with the end of PageRank iterations is decided and a MaxIterations integer that controls the maximum number of PageRank iterations even is no convergence is reached e A section lt DataFiles gt specifying the knowledge bases required by the algorithm This section must contain at least
10. amp encode given sentence in features as integer feature codes and as features names void encode_all const sentence amp std vector lt std set lt std wstring gt gt amp std vector lt std set lt int gt gt amp encode given sentence in features as feature names Return result suitable for Java perl APIs std vector lt std list lt std wstring gt gt encode_name const sentence amp encode given sentence in features as integer feature codes Return result suitable for Java perl APIs std vector lt std set lt int gt gt encode_int const sentence amp 56 CHAPTER 3 ANALYSIS MODULES clear lexicon void clear_lexicon encode sentence and add features to current lexicon void encode_to_lexicon const sentence 4 save lexicon to a file filtering features with low occurrence rate void save_lexicon const std wstring amp double const F The class may be used to encode a corpus and generate a feature lexicon or to encode a corpus filtering the obtained features using a previously generated feature lexicon The used feature rules must follow the format described in section 3 24 1 The rules may call custom feature functions provided the instantianting program provides pointers to call the apropriate code to compute them Once the class is instantiated it can ben used to encode sentences in feature vectors Features may be obtained as strings feature names or integ
11. tokenizer tk path L tokenizer dat splitter sp path L splitter dat morphological analysis has a lot of options and for simplicity they are packed up in a maco_options object First create the maco_options 4 2 SAMPLE PROGRAMS 63 object with default values maco_options opt L es then set required options on off opt UserMap false opt AffixAnalysis true opt MultiwordsDetection true opt NumbersDetection true opt PunctuationDetection true opt Dates Detection true opt QuantitiesDetection false opt DictionarySearch true opt ProbabilityAssignment true opt NERecognition true alternatively you can set active modules in a single call opt set_active_modules false true true true true true false true true true and provide files for morphological submodules Note that it is not necessary to set opt QuantitiesFile since Quantities module was deactivated opt UserMapFile L opt LocutionsFile path L locucions dat opt AffixFile path L afixos dat opt ProbabilityFile path L probabilitats dat opt DictionaryFile path L dicc src opt NPdataFile path L np dat opt PunctuationFile path L common punct dat alternatively you can set the files in a single call opt set_data_files L path L locucions dat L path L afizxos dat PA path L probabilitats dat opt D
12. Section lt SplitMultiwords gt contains only one line with either yes or no If SplitMultiwords is activated Named Entities still will be recognized but they will not be treated as a unit with only one Part of Speech tag for the whole compound Each word gets its own Part of Speech tag instead Capitalized words get the Part of Speech tag as specified in NE_Tag The Part of Speech tags of non capitalized words inside a Named Entity typically prepositions and articles will be left untouched lt SplitMultiwords gt no lt SplitMultiwords gt 3 11 2 BIO NER module bioner The machine learning based NER module uses a classification algorithm to decide whether each word is at a NE begin B inside I or outside 0 Then a simple viterbi algorithm is applied to guarantee sequence coherence It can be instantiated via the ner wrapper described above or directly via its own API class bioner public ner_module public Constructor receives the name of the configuration file bioner const std string amp analyze given sentence void analyze sentence amp analyze given sentences void analyze std list lt sentence gt amp return analyzed copy of given sentence sentence analyze const sentence amp return analyzed copy of given sentences std list lt sentence gt analyze const std list lt sentence gt amp 20 CHAPTER 3 ANALYSIS MODULES The configuration file sets the required mod
13. Since a sentence may have less possible tag sequences than the given k value the results may contain a number of sequences smaller than k The relax_tagger module can be tuned with hand written constraint but is about 2 times slower than hmm_tagger It is not able to produce k best sequences either class relax_tagger public POS_tagger public Constructor given the constraint file and config parameters relax_tagger const std string int double double bool unsigned int analyze given sentence void analyze sentence 4 analyze given sentences void analyze std list lt sentence gt amp return analyzed copy of given sentence sentence analyze const sentence amp return analyzed copy of given sentences std list lt sentence gt analyze const std list lt sentence gt amp The relax_tagger constructor receives the following parameters e The constraint file The format of the file is described below This file can be generated from a tagged corpus using the script src utilities train tagger bin TRAIN sh provided in FreeLing package See src utilities train tagger README for details e An integer stating the maximum number of iterations to wait for convergence before stopping the disambiguation algorithm 3 17 PART OF SPEECH TAGGER MODULE 37 A real number representing the scale factor of the constraint weights A real number representing the threshold under which any changes will
14. Spanish they would not be considered candidates to form named entities Including the form in the lt Names gt section causes the NE choice to be added to the possible tags of the form giving the tagger the chance to decide whether it is actually a verb or a proper noun lt Names gt miren pel Zapatero china lt Names gt Section lt Affixes gt contains a list of words that may be part of a NE either prefixing or suffixing it even if they are lowercased For instance this is the case of the word don in Spanish e g don_Juan should be a NE even if don is lowercased or the word junior or jr in English e g Peter Grasswick_jr should be a NE even if jr is lowercased The section should containt a word per line followed by the keyword PRE or SUF stating whether the word may be attached before or after an NE It a word should be either a prefix or a suffix it must be declared in two different lines one with each keyword lt Affixes gt don PRE do a PRE jr SUF lt Affixes gt Sections lt RE_NounAdj gt lt RE_Closed gt and lt RE_DateNumPunct gt allow to modify the default regular expressions for Part of Speech tags This regular expressions are used by the NER to determine whether a sentence beginning word has some tag that is Noun or Adj or any tag that is a closed category or one of date punctuation number The default is to check against Eagles tags thus the recognizer will fail to identifiy these catego
15. basic or bio in a line enclosed by the tags lt Type gt and lt Type gt The rest of the file must contain the configuration options specific for the selected NER type described below The basic module is simple and fast and easy to adapt for use in new languages provided capitalization is the basic clue for NE detection in the target language The estimated performance of this module is about 85 correctly recognized named entities The bio module is based on machine learning algorithms It has a higher precision over 90 but it is remarkably slower than basic and adaptation to new languages requires a training corpus plus some feature engineering 3 11 1 Basic NER module np The first NER module is the np class which is a just a FSA that basically detects sequences of capitalized words taking into account some functional words e g Bank of England and capitalization at sentence begginings It can be instantiated via the ner wrapper described above or directly via its own API class np public ner_module public automat public Constructor receives a configuration file np const std string amp Detect multiwords starting at given sentence position bool matching sentence amp sentence iterator amp analyze given sentence void analyze sentence 4 analyze given sentences void analyze std list lt sentence gt amp return analyzed copy of given sentence sentence analyze const se
16. comi will match any word analysis with those tag prefix and form Note that the form alone is not allowed in the rule core since the rule woull to distinguish among different analysis of the same form Sense A sense code enclosed in square brackets optionaly preceded by a tag or a wildcarded tag e g 00862617 NCMSO00 00862617 NC 00862617 will match any word analysis with those tag prefix and sense e context is a list of conditions that the context of the word must satisfy for the constraint to be applied Each condition is enclosed in parenthesis and the list and thus the constraint is finished with a semicolon Each condition has the form position terms or either position terms barrier terms Conditions may be negated using the token not i e not pos terms Where position is the relative position where the condition must be satisfied 1 indicates the previous word and 1 the next word A position with a star e g 2 indicates that any word is allowed to match starting from the indicated position and advancing towards the beggining end of the sentence terms is a list of one or more terms separated by the token or Each term may be Plain tag A plain complete PoS tag e g VMIP3S0 Wildcarded tag A PoS tag prefix right wilcarded e g VMI VMIP Lemma A lemma enclosed in angle brackets optionaly preceded by a tag or a wildcarded tag e g lt comer gt VMIP3S0 lt comer gt VMI lt come
17. el DAOMPO 0 975719 lo NCMPOOO 0 00019425 l PP3MPA00 0 024087 gatos gato NCMPOOO 1 Fp 1 If we also wanted PoS tagging we could have issued the command analyze f myconfig cfg outf tagged lt mytext txt gt mytext tag to obtain the tagged output El el DAOMSO gato gato NCMS000 come comer VMIP3S0 pescado pescado NCMS000 Fp Pero pero CC a a SPSOO 70 CHAPTER 5 USING THE SAMPLE MAIN PROGRAM TO PROCESS CORPORA Don_Jaime Don_Jaime NPO0000 no no RN le l PP3CSDOO gustan gustar VMIP3PO los el DAOMPO gatos gato NCMPOOO Fp We can also ask for the senses of the tagged words analyze f myconfig cfg outf sense sense all lt mytext txt gt mytext sen obtaining the output El el DAOMSO gato gato NCMS000 01630731 07221232 01631653 come comer VMIP3S0 00794578 00793267 pescado pescado NCMSO00 05810856 02006311 Fp Pero pero CC a a SPSOO Don_Jaime Don_Jaime NP00000 no no RN le l PP3CSDOO gustan gustar VMIP3P0 01244897 01213391 01241953 los el DAOMPO gatos gato NCMPOOO 01630731 07221232 01631653 Fp Alternatively if we don t want to repeat the first steps that we had already performed we could use the output of the morphological analyzer as input to the tagger analyze f myconfig cfg inpf morfo outf tagged lt mytext mrf gt mytext tag See options InputFormat and OutputFormat in section 5 4 1 for details on which are valid input and output formats 5 4 Configuration File and C
18. file nec nonec NEClassification yes ylon nolnloff Whether to perform NE classification e Named Entity Classifier File Command line Configuration file fnec lt filename gt NECFile lt filename gt Configuration file for Named Entity Classifier module See section 3 19 for details e Phonetic Encoding Command line Configuration file phon nophon Phonetics yeslylon no nloff Whether to add phonetic transcription to each word e Phonetic Encoder File Command line Configuration file fphon lt filename gt PhoneticsFile lt filename gt Configuration file for phonetic encoding module See section 3 18 for details e Sense Annotation Command line Configuration file s lt string gt sense lt string gt SenseAnnotation lt string gt Kind of sense annotation to perform 78 CHAPTER 5 USING THE SAMPLE MAIN PROGRAM TO PROCESS CORPORA no none Deactivate sense annotation all annotate with all possible senses in sense dictionary mfs annotate with most frequent sense ukb annotate all senses ranked by UKB algorithm Whether to perform sense anotation If active the PoS tag selected by the tagger for each word is enriched with a list of all its possible WN synsets The sense repository used depends on the options Sense Annotation Configuration File and UKB Word Sense Disa
19. file q lt int gt queue lt int gt ServerQueueSize lt int gt Specify maximum number of pending clients that the server socket can hold This option is ignored if server mode is off Pending clients are requests waiting for a worker to be available They are queued in the operating system socket queue 72 CHAPTER 5 USING THE SAMPLE MAIN PROGRAM TO PROCESS CORPORA Default value is 32 Note that the operating system has an internal limit for the socket queue size e g modern linux kernels set it to 128 If the given value is higher than the operating system limit it will be ignored When the pending queue is full new incoming requests get a connection error Trace Level Configuration file TraceLevel lt int gt Command line 1 lt int gt tlevel lt int gt Set the trace level 0 no trace higher values more trace for debugging purposes This will work only if the library was compiled with tracing information using configure enable traces Note that the code with tracing information is slower than the code compiled without it even when traces are not active Trace Module Command line m lt mask gt tmod lt mask gt Configuration file TraceModule lt mask gt Specify modules to trace Each module is identified with an hexadecimal flag All flags may be OR ed to specificy the set of modules to be traced Valid masks are defined in file src includ
20. lt locale gt Locale lt locale gt Locale to be used to interpret both input text and data files Usually the value will match the locale of the Lang option e g es_ES utf8 for spanish ca_ES utf8 for Catalan etc The values default stands for en_US utf8 and system stands for currently active system locale may also be used e Splitter Buffer Flushing Command line Configuration file flush noflush AlwaysFlush yes ylon no n off When this option is inactive most usual choice sentence splitter buffers lines until a sentence marker is found Then it outputs a complete sentence When this option is active the splitter never buffers any token and considers each newline as a sentence end thus processing each line as an independent sentence e Input Format Command line Configuration file inpf lt string gt InputFormat lt string gt Format of input data plain token splitted morfo tagged sense plain plain text token tokenized text one token per line splitted tokenized and sentence splitted text one token per line sentences separated with one blank line morfo tokenized sentence splitted and morphologically analyzed text One token per line sentences separated with one blank line Each line has the format word lemma tag prob tagged tokenized sentence splitted morphologically analyzed and PoS tagged text One token p
21. mm is of type LN length and ft s is of type SP speed lt Measure gt CUR USD CUR FRF LN mm SP ft s lt Measure gt Finally section lt MeasureNames gt describes which multiwords have to be interpreted as a mea sure and which unit they represent The unit must appear in section lt Measure gt with its associated code Each line has the format multiword_description code tag where multiword_description is a multiword pattern as in multiwords file described in sec tion 3 10 code is the type of magnitude the unit describes currency speed etc and tag is a constraint on the lemmatized components of the multiword following the same conventions than in multiwords file section 3 10 E g lt MeasureNames gt french_ lt franc gt FRF 2 N lt franc gt FRF 1 N lt dollar gt USD 1 N american_ lt dollar gt USD 2 N us_ lt dollar gt USD 2 N lt milimeter gt mm 1 N lt foot gt _per_second ft s 1 N lt foot gt _Fh_second ft s 1 N lt foot gt _Fh_s ft s 1 N lt foot gt _second ft s 1 N lt MeasureNames gt This section will recognize strings such as the following 234_french_francs CUR_FRF 234 Zm one_dollar CUR_USD 1 Zm two_hundred_fifty_feet_per_second SP_ft s 250 Zu Quantity multiwords will be recognized only when following a number that is in the sentence There were many french francs the multiword won t be recognized since it is not assigning units to a determined quantity It is important
22. one per line Each filename contains a language model generated with the train_language method The filenames may be absolute or relative If relative they are considered to be relative to the location of the identifier options file Section lt Threshold gt and lt ScaleFactor gt contain one single line each consisting of a real number in both cases The identifier uses a 4 gram visible Markov model to compute the probability of the text in each candidate language Since the probabilitity of a sequence depends on its length the result is divided by the text length to obtain a per char averaged probability Even in this way the resulting probability is usually low and unintuitive The parameter ScaleFactor multiplies this result to enlarge the difference between languages and to give probabilities in a more human scale The parameter Threshold states minimun value that a language must achive to be considered a possible result If no language reaches the threshold the identify_language method will return none Note that this scaling is artificial and doesn t change the results only makes them more readable The results with ScaleFactor 1 0 and Threshold 0 04 would be the same than with ScaleFactor 5 0 and Threshold 0 2 An example of a language identifier option file is lt Languages gt es dat ca dat it dat pt dat lt Languages gt lt Threshold gt 0 2 lt Threshold gt lt ScaleFactor gt 5 0 lt ScaleFactor
23. option is described in the following example For instance the word crane has the follwing analysis crane crane NN 0 833 crane VB 0 083 crane VBP 0 083 If the list of senses is simply added to each of them that is DuplicateAnalysis is set to false you will get crane crane NN 0 833 02516101 01524724 crane VB 0 083 00019686 crane VBP 0 083 00019686 But if you set DuplicateAnalysis to true the NN analysis will be duplicated for each of its possible senses crane crane NN 0 416 02516101 crane NN 0 416 01524724 crane VB 0 083 00019686 crane VBP 0 083 00019686 34 CHAPTER 3 ANALYSIS MODULES 3 16 Word Sense Disambiguation Module This module performs word sense disambiguation on content words in given sentences This mod ule is to be used if word sense disambiguation WSD is desired If no disambiguation or basic most frequent sense disambiguation is needed the senses module described in section 3 15 is a lighter and faster option The module is just a wrapper for UKB algorithm AS09 which is integrated in FreeLing and distributed as is under its original GPL license UKB algorithm relies on a semantic relation network in this case WN and XWN to dis ambiguate the most likely senses for words in a text using PageRank algorithm See AS09 for details on the algorithm The module enriches each analysis of each word for the selected PoS with a ranked list of senses The PageRank value is also provided as a result
24. tag unigram bigram and trigram probabilities are used in Linear Interpolation smooth ing by the tagger to compute state transition probabilities 0 parameters of the HMM e Section lt Tag gt List of unigram tag probabilities estimated via your preferred method Each line is a tag probability P t with format Tag Probability Lines for zero tag for initial states and for x unobserved tags must be included E g 0 0 03747 AQ 0 00227 NC 0 18894 x 1 07312e 06 e Section lt Bigram gt List of bigram transition probabilities estimated via your preferred method Each line is a transition probability with the format Tagl Tag2 Probability Tag zero indicates sentence beggining E g the following line indicates the transition probability between a sentence start and the tag of the first word being AQ 0 AQ 0 01403 E g the following line indicates the transition probability between two consecutive tags AQ NC 0 16963 e Section lt Trigram gt List of trigram transition probabilities estimated via your preferred method Each line is a transition probability with the format Tag1 Tag2 Tag3 Probability 38 CHAPTER 3 ANALYSIS MODULES Tag zero indicates sentence beggining E g the following line indicates the probability that a word has NC tag just after a 0 AQ sequence 0 AQ NC 0 204081 E g the following line indicates the probability of a tag SP appearing after two words tagged DA and NC DA NC SP 0 3
25. the dependency parser dep tokenized sentence splitted morphologically analyzed PoS tagged optionally sense annotated and dependency parsed text as output by the second stage trans formation to dependencies and function labelling of the dependency parser e Produce training output format Command line Configuration file train N A When this option only available at command line is specified OutputFormat is forced to tagged and results are printed in the format word lemma tag lemmal tagi lemma2 tag2 that is one word per line with the selected lemma and tag as fields 2 and 3 a separator and a list of all possible pairs lemma tag for the word including the selected one This format is expected by the training scripts Thus this option can be used to an notate a corpus correct the output manually and use it to retrain the taggers with the script src utilities train tagger bin TRAIN sh provided in FreeLing package See src utilities train tagger README for details about how to use it e Language Identification Configuration File Command line Configuration file I lt filename gt fidn lt filename gt N A Configuration file for language identifier See section 3 1 for details e Tokenizer File Command line Configuration file abrev lt filename gt TokenizerFile lt filename gt File of tokenization rules See section 3 2 for details e S
26. to point to a directory with valid FreeLing data files e g usr local share FreeLing Environment variables are used for flexibility but if you don t need them you can replace FREELINGSHARE in your configuration files with a static path The analyzer program provides also a server mode use option server which expects the input from a socket The program analyzer_client can be used to read input files and send requests to the server The advantatge is that the server remains loaded after analyzing each client s request thus reducing the start up overhead if many small files have to be processed Client and server communicate via sockets The client server approach is also a good strategy to call FreeLing from a language or platform for which no API is provided Just launch a server and use you preferred language to program a client that behaves like analyzer_client The analyze no final r script described below handles all these default paths and variables and makes everything easier if you want to use the defaults 5 1 The easy way Using the analyze script To ease the invocation of the program a script named analyze no final r is provided This is script is able to locate default configuration files define library search paths and handle whether you want the client server or the straight version The sample main program is called with the command analyze f config file options If f config file is not specified a fil
27. trabajo or accidentes de trabajo The tag of the multiword will be that of its first form 1 which starts with NC This will assign the right singular plural tag to the multiword depending on whether the form was accidente or accidentes The last field states whether the multiword is ambiguous A or not I with respect to its segmen tation i e that it may be a multiword or not depending on the context The multiword is built in any case but the ambiguity information is stored in the word object so the calling applicacion can consult it and take the necessary decisions if needed 22 CHAPTER 3 ANALYSIS MODULES 3 11 Named Entity Recognition Module There are two different modules able to perform NE recognition They can be instantiated directly or via a wrapper that will create the right module depending on the configuration file The API for the wrapper is the following class WINDLL ner 4 public Constructor ner const std wstring 4 Destructor ner analyze given sentence void analyze sentence 4 analyze given sentences void analyze std list lt sentence gt amp analyze sentence return analyzed copy sentence analyze const sentence 4 analyze sentences return analyzed copy std list lt sentence gt analyze const std list lt sentence gt amp 3 The parameter to the constructor is the absolute name of a configuration file which must contain the desired module type
28. utility is to prevent smoothing of some combinations that are known to have zero probability Lines in this section are trigrams in the same format than above Tag1 Tag2 Tag3 Trigrams listed in this section will be assigned zero probability and no smoothing will be performed This will cause the tagger to avoid any solution including these subsequences The first tag may be a wildcard which will match any tag or the tag 0 which denotes sentence beginning These two special tags can only be used in the first position of the trigram In the case of an EAGLES tagset the tags in the trigram may be either the short or the long version The tags in the trigram except the special tags and 0 can be restricted to a certain lemma suffixing them with the lemma in angle brackets For instance the following rules will assign zero probability to any sequence containing the specified trigram 3 17 PART OF SPEECH TAGGER MODULE 39 PT NC a noun after an interrogative pronoun 0 DT VMI a verb in indicative following a determiner just after sentence beggining SP PP NC a noun following a preposition and a personal pronoun Similarly the set of rules VAI lt haber gt NC VAI lt haber gt AQ VAI lt haber gt VMPOOSF VAI lt haber gt VMPOOPF VAI lt haber gt VMPOOPM dE XX X will assign zero probability to any sequence containing the verb haber tagged as an auxiliar VAI followed by any of the listed tags No
29. your dictionary Dictionaries are created at installation time from sources in the directory data XX dictionary where XX is the language code Those files contain one triplet word lemma tag per line and are fused in a single dictionary at installation time The script in src utilities dicc management bin build dict sh will read the given files and build a dictionary with all them Thus if you have a domain dictionary with a list of triplets word lemma tag you can build a new dictionary fusing the original FreeLing entries with your domain entries The resulting file can be given to the constructor of the dictionary class e Instantiate more than one dictionary module Another option is to instatiate several dictionary modules creating each one with a different dictionary file and run the text through each of them sequentially When the word is found in one dictionary along the chain the following dictionary modules will ignore it and will not attempt to look it up Check the file src utilities dicc management README for details 3 10 MULTIWORD RECOGNITION MODULE 21 3 10 Multiword Recognition Module This module aggregates input tokens in a single word object if they are found in a given list of multiwords The API for this class is class locutions public automat public Constructor receives the name of the file containing the multiwords to recognize locutions const std string amp Detect multiwords st
30. 0 accurate but in some cases e g if you are analyzing newspapers it may be preferrable to the default behaviour which is not 100 accurate either e Section lt SplitMultiwords gt contains only one line with either yes or no If SplitMultiwords is activated Named Entities still will be recognized but they will not be treated as a unit with only one Part of Speech tag for the whole compound Each word gets its own Part of Speech tag instead Capitalized words get the Part of Speech tag as specified in NE_Tag The Part of Speech tags of non capitalized words inside a Named Entity typically prepositions and articles will be left untouched lt SplitMultiwords gt no lt SplitMultiwords gt 3 12 Quantity Recognition Module The quantities class is a FSA that recognizes ratios percentages and physical or currency magnitudes e g twenty per cent 20 one out of five 1 5 one hundred miles per hour etc This module depends on the numbers detection module section 3 5 If numbers are not previously detected and annotated in the sentence quantities will not be recognized This module similarly to number recognition is language dependent That is a FSA has to be programmed to match the patterns of ratio expressions in that language Currency and physical magnitudes can be recognized in any language given the appropriate data file class quantities public Constructor receives the language code and the data file qua
31. 3312 Section lt Initial gt List of initial state probabilities estimated via your preferred method i e the m parameters of the HMM Each line is an initial probability with the format InitialState LogProbability Each InitialState is a PoS bigram code with the form 0 tag Probabilities are given in logarithmic form to avoid underflows E g the following line indicates the probability that the sequence starts with a determiner O DA 1 744857 E g the following line indicates the probability that the sequence starts with an unknown tag 0 x 10 462703 Section lt Word gt Contains a list of word probabilities P w estimated via your preferred method It is used toghether with the tag probabilities above to compute emission prob abilities biw parameters of the HMM Each line is a word probability P w with format word LogProbability A special line for lt UNOBSERVED_WORD gt must be included Sample lines for this section are afortunado 13 69500 sutil 13 57721 lt UNOBSERVED_WORD gt 13 82853 Section lt Smoothing gt contains three lines with the coefficients used for linear interpolation of unigram c1 bigram c2 and trigram c3 probabilities The section looks like lt Smoothing gt c1 0 120970620869314 c2 0 364310868831106 c3 0 51471851029958 lt Smoothing gt Section lt Forbidden gt is the only that is not generated by the training scripts and is supposed to be manually added if needed The
32. FreeLing User Manual 3 0 May 2012 Contents 1 Introduction kt Wihatiis Freeling si wi r K a A AO A ae ow Se ah eee K 12 What is NOT FreeLing i Di X a weg Ra RRR a a E ded 1 3 Supported Languages ee LAES CESE Z a aT ir pd A E A GN ai e A 1 5 Contributions euro ra A A a ck a Ad 2 Getting it to work 2 1 Reqiirements vna e we ade aaa a e a 9 2 2 VInstall tion iaa a a a ps a 2 3 EXECULIDE ko a a A ee o ee a A 2 4 Porting to other platforms 3 Analysis Modules 3 1 Language Identifier Module o e 3 2 Tokenizer Modules ovocitos id aa a 3 3 Splitter Module N RRR a e ee e a ee 3 4 Morphological Analyzer Module o 3 5 Number Detection Module 0 0 00 eee ee 3 6 Punctuation Detection Module o e s User Map Module X a ei TE a ee Dad IAE 3 8 Dates Detection Module e 3 9 Dictionary Search Module e 3 10 Multiword Recognition Module o e 3 11 Named Entity Recognition Module o o e e e 3 12 Quantity Recognition Module 3 13 Probability Assignment and Unkown Word Guesser Module 3 14 Ortographic Correction Module 3 15 Sense Labelling Module o e 3 16 Word Sense Disambiguation Module o o e e e 3 17 Part o Speech Tagger Module
33. FreeLing uses en_US UTF8 locale as default during installation If this locale is not installed in your system you ll get an error during dictionary installation Most languages in FreeLing will work with this locale but Russian will need to have its own locale installed in the system The procedure to install a locale in your system varies depending on your distribution For instance e In Ubuntu you must use the locale get command E g sudo locale gen en_US UTF8 sudo locale gen pt_BR UTF8 sudo locale gen ru_RU UTF8 e In Debian you need to run the command dpkg reconfigure locales and select the desired locales from the list 2 3 Executing FreeLing is a library which means that it not a final user oriented executable program but a tool to develop new programs which may require linguistic analysis services Nevertheless a sample main program is included in the package for those who just want a text analyzer This program may be adapted to fit your needs e g customized input output formats The usage and options of this main program is described in chapter 5 Please take into account that this program is only a friendly interface to demonstrate FreeLing abilities but that there are many other potential usages of FreeLing Thus the question is not why this program doesn t offer functionality X why it doesn t output information Y or why it doesn t present results in format Z but How should I use FreeLing to write a p
34. GRLAB gt rules section lt SEMDB gt must be defined before section lt GRLAB gt The section must contain a single line specifying a configuration file for a semanticDB object The filename may be absolute or relative to the location of the dependency rules file lt SEMDB gt semdb dat lt SEMDB gt The configuration file must follow the format described in section 3 23 Class definitions Section lt CLASS gt contains class definitions which may be used as attributes in the dependency labelling rules Each line contains a class assignation for a lemma with two possible formats class name lemma comments class name filename comments For instance the following lines assign to the class mov the four listed verbs and to the class animal all lemmas found in animals dat file In the later case if the file name is not an absolute path it is interpreted as a relative path based at the location of the rule file Anything to the right of the second field is considered a comment and ignored mov go prep to towards mov come prep from mov walk prep through mov run prep to towards D O animal animals dat 52 CHAPTER 3 ANALYSIS MODULES 3 22 Coreference Resolution Module This module is a machine learning based coreference solver following the algorithm proposed by SNLO1 It takes a document parsed by the shallow parser to detect noun phrases and decides which noun phrases are coreferential The api of the mo
35. NCY PARSER MODULE 49 e last_left The right subtree is added as a daughter of the last node inside the left subtree matching label value or to the root if none is found The root of the new tree is the root of the left subtree e top_right The left subtree is added as a new daughter of the right subtree The root of the new tree is the root of the right subtree If a label value other than is specified the root is relabelled with that string e last_right The left subtree is added as a daughter of the last node inside the right subtree matching label value or to the root if none is found The root of the new tree is the root of the right subtree e cover_last_left The left subtree s takes the position of the last node x inside the right subtree matching label value The node x is hanged as new child of s The root of the new tree is the root of the right subtree The context may be specified as a sequence of chunk labels separated by underscores _ One of the chunk labels must be and refers to the pair of chunks which the rule is being applied to For instance the rule 20 _vp np pp lt of gt top_left RELABEL would add the rightmost chunk in the pair pp lt of gt under the leftmost np only if the chunk immediate to the right of the pair is labeled vp Other admitted labels in the context are matching exactly one chunk with any label matching zero or more chunks with any label and OUT m
36. P that this entry may have in the dictionary and el will get any tag starting with DA 3 9 DICTIONARY SEARCH MODULE 19 Note that a contraction included in the dictionary cannot be splitted in two different ways corresponding to different forms e g he s he is hethas so only a combination of forms and a combination of tags may appear in the dictionary Nevertheless a set of tags may be specified for a given form e g he d het d PRP VB MD This will produce two words he with PRP analysis and d with its analysis matching any of the two given tags i e have_VBZ and would_MD Note that this will work only if the form d is found in the dictionary with those possible analysis If all tags for one of the new forms are to be used a wildcard may be written as a tag e g pal paratel SP This will replace pal with two words para with only its SP analysis plus el with all its possible tags The contraction entries in the dictionary are intended for inambiguous contractions or for cases such that it is not worth or it is too difficult to handle otherwise For splitting more sophisticated compound words such as verb clitic pronouns in Spanish or Italian e g dale dar l derivation e g quick quickly r pida r pidamente diminutive augmentative sufixes prefixes or other similar behaviours the affixation module should be used see section 3 9 2 for details An optional parameter in the constructor enables to cont
37. a knowledge based constraint grammar Nevertheless the easiest way if you have an annotated corpus is using the estimation and smoothing script src utilities train tagger bin TRAIN sh provided in FreeLing source pack age The produced constraint grammar files contain only simple bigram constraints but the model may be improved by hand coding more complex context constraint as can be seen in the Spanish data file in share FreeLing es constr_grammar dat 6 5 Named Entity Recognizer and Classifier Named Entity recognition and classification modules can be trained for a new language provided a hand annotated large enough corpus is available A README file and training scripts can be found in src utilities nerc in FreeLing tarball 86CHAPTER 6 EXTENDING THE LIBRARY WITH ANALYZERS FOR NEW LANGUAGES 6 6 Chart Parser The parser only requires a grammar which is consistent with the tagset used in the morphological and tagging steps The grammar file must be specified in the GrammarFile option or passed to the parser constructor See section 3 20 1 for format details 6 7 Dependency Parser The depencency parser only requires a set of rules which is consistent with the PoS tagset and the non terminal categories generated by the Chart Parser grammar The grammar file must be specified in the DepRulesFile option or passed to the parser constructor See section 3 21 1 for format details Bibliography ACMO5 AS09 Bra00 CL11
38. a custom feature function It must have the format functname position T where functname is the name of the function as declared in the custom feature functions map see section 3 24 5 The position parameter is the relative position to the focus word and is interpreted in the same way than in the primitive features w position t position etc described above E g the pattern quoted 1 _ quoted 0 would generate a feature similar to that of the pattern t 1 _t O but using the result of the custom function quoted instead of the PoS tag for the corresponding word Feature Rules Condition Syntax Conditions control the applicability of a rule or a rule package A condition may be ALL which is satisfied by any word A condition may be simple or compund of several conditions combined with the logical operadors AND and OR The operators in a condition must be homogeneous i e either all of them AND or all of them OR mixed conditions are not allowed note that an OR condition is equivalent to writing two rules that only differ on the condition Single conditions consist of a word property an operation and an argument Available word properties are W W 1 t T Word form original casing Word form lowercased Lemma of the analysis selected by the tagger PoS tag short version of the analysis selected by the tagger PoS tag full version of the analysis selected by the tagger pl List of all pos
39. ables refer to the analysis selected by the tagger Variable names may be prefixed with p e g pT pl pa etc which will generate the feature for all possible analysis of the word not just the one selected by the tagger The position indicates from which word relative to the focus word the value for the variable must be taken For instance the pattern pbig w 0 _ pt 1 will extract features that will contain the relative position plus a bigram made of the word form of the current word in the window w 0 plus each possible PoS tag of the word right of it pt 1 In the sentence John lives here the features for word here in a window of 2 0 with the above pattern would be pbig 2 john_VBZ pbig 2 john_NNS pbig 1 lives_RB and pbigC0 here_Fp Note that there are two features generated for window position 2 because the word lives has two possible PoS tags 58 CHAPTER 3 ANALYSIS MODULES e Curly brackets have two possible interpretations depending on what they contain 3 24 4 1 If the brackets enclose a regex match variable e g 0 1 2 then they are replaced with the strig matching the corresponding sub expression This only makes sense if the condition of the rule included a regular expression match see section 3 24 4 If it is not the case results are undefined probably a segmentation violation If the bracket content is not a regex match variable then it is interpreted as call to
40. according to mapping rules void get_WN_keys const std wstring amp const std wstring amp const std wstring amp std list lt std pair lt std wstring std wstring gt gt amp const get list of words for a sense pos std list lt std string gt get_sense_words const std string amp const std string amp const get list of senses for a lemmatpos std list lt std string gt get_word_senses const std string amp const std string amp const get sense info for a sensecode pos sense_info get_sense_info const std string amp const std string amp const The constructor receives a configuration file with the following contents e A section lt WNPosMap gt which establishes which PoS found in the morphological dictionary should be mapped to each WN part of speech Rule format is described in section 3 23 1 e A section lt DataFiles gt specifying the knowledge bases required by the algorithm This section may contain up to three keywords with the format lt DataFiles gt senseDictFile senses30 src wnFile common wn30 src formDictFile dicc src lt DataFiles gt senseDictFile is the sense repository with the format described in section 3 23 2 54 CHAPTER 3 ANALYSIS MODULES wnFile is a file stating hyperonymy relations and other semantic information for each sense The format is described in section 3 23 3 formDictFile may be needed if mapping rules in lt WNPosMap gt re
41. ages accentuation in a root obtained after removing a given suffix A good idea to start with this issue is having a look at the accents_es class 6 2 7 Probability assignment The module in charge of assigning lexical probabilities to each word analysis only requires a data file referenced by the ProbabilityFile configuration option This file may be created using the script src utilities train tagger bin TRAIN sh in cluded in FreeLing source package and a tagged corpus See section 3 13 for format details 6 3 HMM PoS Tagger The HMM PoS tagger only requires an appropriate HMM parameters file given by the Tag gerHMMFile option See section 3 17 1 for format details To build a HMM tagger for a new language you will need corpus preferably tagged and you will have to write some probability estimation scripts e g you may use MLE with a simple add one smoothing Nevertheless the easiest way if you have a tagged corpus is using the estimation and smooth ing script src utilities train tagger bin TRAIN sh provided in FreeLing source package 6 4 Relaxation Labelling PoS Tagger The Relaxation Labelling PoS tagger only requires an appropriate pseudo constraint grammar file given by the RelaxTaggerFile option See section 3 17 2 for format details To build a Relax tagger for a new language you will need corpus preferably tagged and you will have to write some compatibility estimation scripts You can also write from scratch
42. al options for the splitter Namely AllowBetweenMarkers and MaxWords options The former may take values 1 or 0 on off The later may be any integer An example of the lt General gt section is lt General gt AllowBetweenMarkers 0 MaxWords 0 lt General gt If AllowBetweenMarkers is off 0 a sentence split will never be introduced inside a pair of parenthesis like markers which is useful to prevent splitting in sentences such as I hate Mary said Angryly apple pie If this option is on 1 sentence splits will be introduced as if they had happened outside the markers MaxWords states how many words are processed before forcing a sentence split inside parenthesis like markers this option is intended to avoid memory fillups in case the markers are not properly closed in the text A value of zero means Never split I ll risk to a memory fillup This option is less aggressive than unconditionally activating AllowBetweenMarkers since it will introduce a sentence split between markers only after a sentence of length MaxWords has been accumulated Setting MaxWords to a large value will prevent memory fillups while keeping at a minimum the splittings inside markers The lt Markers gt section lists the pairs of characters or character groups that have to be considered open close markers For instance lt Markers gt L lt Markers gt The lt SentenceEnd gt section lists which characters are c
43. al symbols in decreasing priority order the later in the list the lower priority When a top cell can be covered with two different non terminals the one with highest priority is chosen This has no effect on non top cells in fact if you want that your grammar is probably ambiguous and you should rethink it 3 21 Dependency Parser Module The Txala dependency parser ACM05 gets parsed sentences that is sentence objects which have been enriched with a parse_tree by the chart_parser or by any other means class dep_txala public dependency_parser public constructor dep_txala const std string amp const std string amp analyze given sentence void analyze sentence amp analyze given sentences void analyze std list lt sentence gt amp return analyzed copy of given sentence sentence analyze const sentence amp return analyzed copy of given sentences std list lt sentence gt analyze const std list lt sentence gt amp 3 The constructor receives two strings the name of the file containging the dependency rules to be used and the start symbol of the grammar used by the chart_parser to parse the sentence The dependency parser works in three stages 3 21 DEPENDENCY PARSER MODULE 47 e At the first stage the lt GRPAR gt rules are used to complete the shallow parsing produced by the chart into a complete parsing tree The rules are applied to a pair of adjacent chunks At
44. ange in the format num num and states the words around the target word for which the feature has to be computed A window of 0 0 means that the feaure is only checked for the target word e condition is the condition that a word has to satisfy in order to get the features extracted by the rule See section 3 24 4 for a description of condition syntax 3 24 3 Feature Name Pattern Syntax Each feature rule has a feature name pattern that describes how the generated feature name will be The following characters are special and are interpreted as variables and replaced by the corresponding values e Character will be replaced with the relative position of the matching word with respect to the target word Thus the rule punct_mark 2 2 t matches F will generate a different feature for each word in the window that is a punctuation sign e g punct_mark 2 and punct_mark 1 for the word John in the avobe example But the rule punct_mark 2 2 t matches F will generate the same feature for all words in the window that are punctuation signs e g it will generate punct_mark twice for the word John in the avobe example Repeated features are stored only once e Character introduces a variable that must have the format var position Allowed variable names are W word form in its original casing w word form lowercased 1 word lemma T word full PoS tag t word short PoS tag a word lemma Pos tag All above vari
45. arting at given sentence position bool matching sentence amp sentence iterator amp analyze given sentence void analyze sentence 4 analyze given sentences void analyze std list lt sentence gt amp return analyzed copy of given sentence sentence analyze const sentence amp return analyzed copy of given sentences std list lt sentence gt analyze const std list lt sentence gt amp Class automat implements a generic FSA The locutions class is a derived class which imple ments a FSA to recognize the word patterns listed in the file given to the constructor 3 10 1 Multiword Definition File The file contains a list of multiwords to be recognized The format of the file is one multiword per line Each line has the format form lemmal posi lemma2 pos2 AII The multiword form may admit lemmas in angle brackets meaning that any form with that lemma will be considered a valid component for the multiword Any number of pairs lemma tag may be assigned to the multiword The PoS tagger will select the most appropriate as with any other word For instance a_buenas_horas a_buenas_horas RG A a_causa_de a_causa_de SPSOO I lt accidente gt _de_trabajo accidente_de_trabajo 1 NC I The tag may be specified directly or as a reference to the tag of some of the multiword components In the previous example the last multiword specification will build a multiword with any of the forms accidente de
46. as active the sense tagger duplicates the analysis creating a new entry for each sense So when a rule selects an analysis having a certain sense it is unselecting the other copies of the same analysis with different senses 3 18 PHONETIC ENCODING MODULE Al Examples Examples The next constraint states a high incompatibility for a word being a definite determiner DA if the next word is a personal form of a verb VMI 8 143 DA 1 VMI The next constraint states a very high compatibility for the word mucho much being an indefinite determiner DI and thus not being a pronoun or an adverb or any other analysis it may have if the following word is a noun NC 60 0 DI mucho 1 NC The next constraint states a positive compatibility value for a word being a noun NC if somewhere to its left there is a determiner or an adjective DA or AQ and between them there is not any other noun 5 0 NC 1 DA or AQ barrier NC The next constraint states a positive compatibility value for a word being a masculine noun NCM if the word to its left is a masculine determiner It refers to a previously defined SET which should contain the list of all tags that are masculine determiners This rule could be useful to correctly tag Spanish words which have two different NC analysis differing in gender e g el cura the priest vs la cura the cure 5 0 NCM 1 DetMasc The next constraint adds some positive compatibili
47. ass senses public Constructor receives the name of the configuration file senses const std string 4 analyze given sentence void analyze sentence amp analyze given sentences 3 15 SENSE LABELLING MODULE 33 void analyze std list lt sentence gt amp return analyzed copy of given sentence sentence analyze const sentence amp return analyzed copy of given sentences std list lt sentence gt analyze const std list lt sentence gt amp The constructor of this class receives the name of a configuration file which is expected to contain the following sections e A section lt WNposMap gt with the mapping rules of FreeLing PoS tags to sense dictionary PoS tags See details in section 3 23 A section lt DataFiles gt containing at least the keyword SenseDictFile defined to a valid sense dictionary file name E g lt DataFiles gt SenseDictFile senses30 src lt DataFiles gt The sense dictionary must follow the format described in section 3 23 2 If the mapping rules lt WNposMap gt require a form dictionary a keyword formDictFile with the dictionary to use must be provided in this section More details are given in section 3 23 A section lt DuplicateAnalysis gt containing a single line with either yes or no stating whether the analysis with more than one senses must be duplicated If this section is ommitted no is used as default value The effect of activating this
48. at ProbabilityThreshold 0 001 NER options NERecognition yes NPDataFile FREELINGSHARE es np dat comment line above and uncomment that below if you want a better NE recognizer higer accuracy lower speed NPDataFile FREELINGSHARE es ner ner ab dat Spelling Corrector config file CorrectorFile FREELINGSHARE es corrector corrector dat Phonetic encoding of words Phonetics no PhoneticsFile FREELINGSHARE es phonetics dat NEC options NEClassification no NECFile FREELINGSHARE es nec nec svm dat Sense annotation options none all mfs ukb SenseAnnotation none SenseConfigFile FREELINGSHARE es senses dat UKBConf igFile FREELINGSHARE es ukb dat Tagger options Tagger hmm TaggerHMMFile FREELINGSHARE es tagger dat TaggerRelaxFile FREELINGSHARE es constr_gram dat TaggerRelaxMaxIter 500 TaggerRelaxScaleFactor 670 0 TaggerRelaxEpsilon 0 001 TaggerRetokenize yes TaggerForceSelect tagger Parser options GrammarFile FREELINGSHARE es grammar dep dat Dependence Parser options DepTxalaFile FREELINGSHARE es dep dependences dat Coreference Solver options CoreferenceResolution no CorefFile FREELINGSHARE es coref coref dat Chapter 6 Extending the library with analyzers for new languages It is possible to extend the library with capability to deal with a new language In some cases this may be done without reprogramming but for accurate results some modules would require enterin
49. atching a sentence boundary For instance the context np_ _ _vp_ _OUT would match a sentence in which the focus pair of chunks is immediately after an np and the second to last chunk is labeled vp Context conditions can be globally negated preceding them with an exclamation mark E g Inp_ _ _vp would cause the rule to be applied only if that particular context is not satisfied Context condition components may also be individually negated preceding them with the symbol E g the rule np_ _ vp would be satisfied if the preceding chunk is labeled np and the following chunk has any label but vp Enabling flags may be defined and used at the grammarian s will For instance the rule 20 INIT PH1 _vp np pp lt of gt last_left MATCHING npms animal PH2 INIT PH1 Will be applied if either INIT or PH1 flags are on the chunk pair is a np followed by a pp with head lemma of and the context one vp chunk following the pair is met Then the deepest rightmost node matching the label npms animal will be sought in the left chunk and the right chunk will be linked as one of its children If no such node is found the rule will not be applied After applying the rule the flag PH2 will be toggled on and the flags INIT and PH1 will be toggled off The only predefined flag is INIT which is toggled on when the parsing starts The grammarian can define any alphanumerical string as a flag simply toggling it on in some rule Dependency fu
50. aths for the files either relative or absolute instead of using variables default configuration file for Spanish analyzer TraceLevel 3 TraceModule 0x0000 Options to control the applied modules The input may be partially processed or not a full analysis may me wanted The specific formats are a choice of the main program using the library as well as the responsability of calling only the required modules Valid input formats are plain token splitted morfo tagged sense Valid output formats are plain token splitted morfo tagged shallow parsed dep InputFormat plain DutputFormat tagged consider each newline as a sentence end AlwaysFlush no Tokenizer options TokenizerFile FREELINGSHARE es tokenizer dat Splitter options SplitterFile FREELINGSHARE es splitter dat Morfo options AffixAnalysis yes MultiwordsDetection yes NumbersDetection yes PunctuationDetection yes DatesDetection yes QuantitiesDetection yes DictionarySearch yes ProbabilityAssignment yes OrthographicCorrection no DecimalPoint ThousandPoint LocutionsFile FREELINGSHARE es locucions dat QuantitiesFile FREELINGSHARE es quantities dat AffixFile FREELINGSHARE es afixos dat ProbabilityFile FREELINGSHARE es probabilitats dat 82 CHAPTER 5 USING THE SAMPLE MAIN PROGRAM TO PROCESS CORPORA DictionaryFile FREELINGSHARE es dicc src PunctuationFile FREELINGSHARE common punct d
51. bboost filesystem dev libboost program options dev sudo dpkg i FreeLing 3 0 deb The first two lines above should install both header packages and binary libraries If it doesn t use your package manager to install them In a Debian system the above commands must be issued as root and without sudo 2 2 2 Install from tar gz source packages Installation from source follows standard GNU autoconfigure installation procedures that is the usual configure amp amp make amp amp make install stuff Installing from source is slower and harder but it will work in any Linux box even if you have library versions different than those required by the deb package 1 Install development tools You ll need to install the C compiler sudo apt get install build essential automake autoconf In Debian use the same command as root without sudo In other distributions check the distribution package manager to install a working C compiler 2 Install packaged requirements Most of the required libraries are standard packages in all Linux distributions Just open your favorite software package manager and install them Package names may vary slightly in different distributions See section 2 1 for some hints on possible package names As an example commands to install the packages from command line in Ubuntu and Debian are provided though you can do the same using synaptic or aptitude If you have another distribution use your pa
52. be considered too small Used to detect convergence e A boolean stating whether words that carry retokenization information e g set by the dictionary or affix handling modules must be retokenized that is splitted in two or more words after the tagging e An integer stating whether and when the tagger must select only one analysis in case of ambiguity Possbile values are FORCE_NONE or 0 no selection forced words ambiguous after the tagger remain ambiguous FORCE_TAGGER or 1 force selection immediately after tagging and before retokenization FORCE_RETOK or 2 force selection after retok enization The iteration number scale factor and threshold parameters are very specific of the relaxation labelling algorithm Refer to Pad98 for details 3 17 1 HMM Tagger Parameter File This file contains the statistical data for the Hidden Markov Model plus some additional data to smooth the missing values Initial probabilities transition probabilities lexical probabilities etc The file may be generated by your own means or using a tagged corpus and the script src utilities train tagger bin TRAIN sh provided in FreeLing package See src utilities train tagger RE for details The file has seven sections lt Tag gt lt Bigram gt lt Trigram gt lt Initial gt lt Word gt lt Smoothing gt and lt Forbidden gt Each section is closed by it corresponding tag lt Tag gt lt Bigram gt lt Trigram gt etc The
53. ce void analyze sentence amp analyze given sentences void analyze std list lt sentence gt amp return analyzed copy of given sentence sentence analyze const sentence 4 return analyzed copy of given sentences std list lt sentence gt analyze const std list lt sentence gt amp F The maco_options class has the following API class maco_options public Language analyzed std string Lang Submodules to activate bool UserMap AffixAnalysis MultiwordsDetection NumbersDetection PunctuationDetection DatesDetection QuantitiesDetection DictionarySearch ProbabilityAssignment OrthographicCorrection NERecognition 14 CHAPTER 3 ANALYSIS MODULES Names of data files to provide to each submodule std string UserMapFile LocutionsFile QuantitiesFile AffixFile ProbabilityFile DictionaryFile NPdataFile PunctuationFile CorrectorFile module specific parameters for number recognition std wstring Decimal Thousand module specific parameters for probabilities double ProbabilityThreshold module specific parameters for dictionary bool InverseDict RetokContractions constructor maco_options const std string amp Option setting methods provided to ease perl interface generation Since option data members are public and can be accessed directly from C the following methods are not necessary but may become convenient sometimes T
54. cess to enable the use of get_forms to obtain form given lemma pos e An optional boolean default true stating whether the contractions found in the dictionary must be retokenized right away or left for later modules to decide 3 9 1 Form Dictionary File The form dictionary is a plain text file An optional first line may exists with one of the strings DB_PREFTREE or DB_MAP If ommitted a default value of DB_MAP is used With DB_MAP the dictionary is stored in a C STL map container With DB_PREFTREE it is stored in a prefix tree structure Depending on the size of the dictionary and on the morphological variation of the language one structure may yield slightly better access times than the other The rest of the lines contain one form per line Each form line has format form lemma1 PoS1 lemma2 PoS2 E g casa casa NCFS000 casar VMIP3S0 casar VMM02S0 backs back NNS back VBZ Lines corresponding to words that are contractions may have an alternative format if the contrac tion is to be splitted The format is form formi form2 PoS1 PoS2 where form1 form2 are the forms not the lemmas of the contracted words For instance del de el SP DA This line expresses that whenever the form del is found it is replaced with two words de and el Each of the new two word forms are searched in the dictionary and assigned any tag matching their correspondig tag in the third field So de will be assigned all tags starting with S
55. ckage manager to locate and install the appropriate library packages see section 2 1 Both in Debian squeeze or wheezy and in Ubuntu Lucid or later you need to do sudo apt get install libboost regex dev libicu dev sudo apt get install libboost filesystem dev libboost program options dev 3 Install FreeLing tar xzvf FreeLing 3 0 tar gz cd freeling 3 0 configure make sudo make install 2 2 INSTALLATION 7 FreeLing library is entirely contained in the file libfreeling so installed in usr local lib by default Sample program analyze is installed in usr local bin See sections 2 3 and 5 for details 2 2 3 Install from SVN repositories Installing from the SVN is very similar to installing from source but you ll have the chance to easily update your FreeLing to the latest development version 1 Install development tools You ll need to install the C compiler the GNU autotools and a SVN client sudo apt get install build essential automake autoconf libtool subversion If you use a distribution different than Debian or Ubuntu these packages may have different names Use your package manager to locate and install the appropriate ones 2 Install packaged requirements Follow the same procedure described in section 2 2 2 for this step 3 Checkout FreeLing sources If you want the latest development version do svn checkout http devel cpl upc edu freeling svn trunk myfreeling you can replace myfreeling with
56. ctionary file for the dictionary search module or a CFG grammar for a parser module Most modules are language independent that is if the provided file contains data for another language the same module will be able to process that language If an application needs to process more than one language it can instantiate the needed modules for each language simply calling the constructors with different data files as a parameter 3 1 Language Identifier Module This module is somehow different of the other modules since it doesn t enrich the given text It compares the given text with available models for different languages and returns the most likely language the text is written in It can be used as a preprocess to determine which data files are to be used to analyze the text The API of the language identifier is the following class lang_ident 4 public Build a language identifier read options from given file lang_ident const std wstring 4 load given language from given file add to existing languages void add_language const std wstring amp train a model for a language store in modelFile and add it to the known languages list void train_language const std wstring amp const std wstring amp const std wstring amp Classify the input text and return the code of the best language or none std wstring identify_language const std wstring amp const std set lt std wstring gt amp const
57. d time expression detection Quantities Detection Command line Configuration file quant noquant QuantitiesDetection yes y on no n off Whether to perform currency amounts physical magnitudes and ratio detection Quantity Recognition File Command line Configuration file Q lt filename gt fqty lt filename gt QuantitiesFile lt filename gt Quantitiy recognition configuration file See section 3 12 for details Dictionary Search Command line Configuration file dict nodict DictionarySearch yeslylon no nloff Whether to search word forms in dictionary Deactivating this feature also deactivates AffixAnalysis option Dictionary File Command line Configuration file D lt filename gt fdict lt filename gt DictionaryFile lt filename gt Dictionary database See section 3 9 and chapter 6 for details Probability Assignment Command line Configuration file prob noprob ProbabilityAssignment yeslylonlnolnloff Whether to compute a lexical probability for each tag of each word Deactivating this feature will affect the behaviour of the PoS tagger Lexical Probabilities File Command line Configuration file P lt filename gt fprob lt filename gt ProbabilityFile lt filename gt Lexical probabilities file The probabilities in this file are used to compute the most likely tag for a word as
58. dule is the following class coref 4 3 public Constructor coref const std string amp const int Classify in coreference chains noun phrases in given document void analyze document amp const The parameters received by the constructor are a filename and an integer bitmask specifying which attributes have to be used by the classifier The meaning of the attributes can be found in the source file include freeling coref_fex h If you just want to use the module set the value of this parameter to OxFFFFFF to select all the attributes The string parameter is the name of the configuration file which is described below 3 22 1 Coreference Solver configuration file The Coreference Solver module reads this file to find out some needed parameters The file has three sections e Section lt ABModel gt path to the file containing the trained AdaBoost model The abm file contains an AdaBoost model based on shallow Decision Trees see CMP03 for details You don t need to understand this unless you want to enter into the code of the AdaBoost classifier The name of the file may be either absolute or relative to the location of the Coreference Solver config file e g lt ABModel gt Coret abm lt ABModel gt It may be generated from an annotated corpus using the training programs that can be found in src utilities coref If you need to know more about this e g to develop a Coreference Solver f
59. e freeling morfo traces h and are the fol lowing Module Mask Splitter 0x00000001 Tokenizer 0x00000002 Morphological analyzer 0x00000004 Options management 0x00000008 Number detection 0x00000010 Date identification 0x00000020 Punctuation detection 0x00000040 Dictionary search 0x00000080 Affixation rules 0x00000100 Multiword detection 0x00000200 Named entity detection 0x00000400 Probability assignment 0x00000800 Quantities detection 0x00001000 Named entity classification 0x00002000 Automata abstract 0x00004000 Sense annotation 0x00010000 Chart parser 0x00020000 Parser grammar 0x00040000 Dependency parser 0x00080000 Correference resolution 0x00100000 Utilities 0x00200000 Word sense disambiguation 0x00400000 Ortographic correction 0x00800000 Database storage 0x01000000 Feature extraction 0x02000000 Language identifier 0x04000000 Omlet 0x08000000 Phonetics 0x10000000 e Language of input text Command line Configuration file lang lt language gt Lang lt language gt 5 4 CONFIGURATION FILE AND COMMAND LINE OPTIONS 73 Code for language of input text Though it is not required the convention is to use two letter ISO codes as Asturian es Spanish ca Catalan en English cy Welsh it Italian gl Galician pt Portuguese ru Russian old es ancient Spanish Other languages may be added to the library See chapter 6 for details e Locale Command line Configuration file locale
60. e named analyzer cfg is searched in the current working directory 67 68 CHAPTER 5 USING THE SAMPLE MAIN PROGRAM TO PROCESS CORPORA If f config file is specified but not found in the current directory it will be searched in FreeLing installation directory usr local share FreeLing conf ig if you installed from source and usr share FreeLing config if you used a binary deb package Extra options may be specified in the command line to override any settings in config file See section 5 4 1 for details Server mode is triggered by option server See section 5 4 1 for details 5 1 1 Stand alone mode The default mode will launch a stand alone analyzer which will load the configuration read input from stdin write results to stdout and exit E g analyze f en cfg lt myinput gt myoutput When the input file ends the analyzer will stop and it will have to be reloaded again to process a new file The above command is equivalent to 1 Define FREELINGSHARE variable to point to a directory with FreeLing data e g usr local share freeling 2 Make sure the LD_LIBRARY_PATH variable contains the directory where FreeLing libraries are installed e g usr local 1ib 3 Locate the given configuration file in the current directory or in the FreeLing installation directories e g usr local share freeling config 4 Run the analyzer final r program analyzer f usr local share freeling config en cfg lt myinput gt myoutpu
61. e needed resulting in a higher computational cost The package condition may be ALL In this case rules contained in the package will be checked for all words in the sentence This condition has also an extra effect the features extracted by rules in this package are cached in order to avoid repeating computations if a rule uses a window to get features from neighbour words For instance the rule RULES ALL punct_mark 2 2 t matches F ENDRULES will generate for each word features indicating which words in the surrounding two words left and right are punctuation symbols 3 24 FEATURE EXTRACTION MODULE 57 With this rule applied to the sentence Hi said John the word said would get the fea tures punct_mark 1 punct_mark 2 and punct_mark 2 The word John would get the features punct_mark 2 and punct_mark 1 Since the package has condition ALL the features are com puted once per word and then reused that is the fact that the comma is a punctuation sign will be checked only once regardless of the size of the sentence and the size of the windows in the rules 3 24 2 Rule Syntax Each rule has following syntax feature name pattern window condition e feature name pattern is a string that describes what the generated feature name will be Some special characters allow the insertion of variable values in the feature name See section 3 24 3 for a description of the syntax of feature name patterns e window is a r
62. e part of the input or output alphabet to avoid ambiguities e g U aeiou V aeioud isiid i6it0 L lt 99100961 0099100000 A a E eg Categories are only active for rules in the same ruleset where the category is defined Rules are of the form source target context Both source and target may be either a category name or a terminal string Simple string rules replace the first string with the second if and only if it occurs in the given context Contexts must contain a _ symbol indicating where the source string is located They may also contain characters categories and the symbols word beginning or word end The empty context _ is always satisfied Rules can only change terminal strings to terminal strings or categories to categories i e both source and target have to be of the same type If a category is to be changed to another category they should contain the same number of characters Otherwise the second category will have its last letter repeated until it has the same length as the first if it is shorter or characters in the second category that don t match characters in the first will be ignored Some example rules for English qu kw _ h r_ a 0 _1mV U L C_CV 3 19 NAMED ENTITY CLASSIFICATION MODULE 43 First rule qu kw _ replaces string qu with kw in any context Second rule h r_ removes character h when it is preceeded by r at word beginning Rule a d _1mV replaces a with when fo
63. e will be used only for your language feel free to readjust its format 6 2 5 Dictionary search The lexical forms for each language are sought in a database You only have to specify in which file it is found with the DictionaryFile option The dictionary file can be build with the indexdict program you ll find in the binaries directory of FreeLing This program reads data from stdin and indexes them into a DB file with the name given as a parameter The input data is expected to contain one word form per line each line with the format form lemmai tag lemma2 tag2 E g abalanzar abalanzar VMIC1SO abalanzar VMIC3S0 bajo bajar VMIP1SO bajo AQOMSO bajo NCMSO00 bajo SPSOO efusivas efusivo AQOFPO 6 3 HMM POS TAGGER 85 6 2 6 Affixed forms search Forms not found in dictionary may be submitted to an affix analysis to devise whether they are derived forms The valid affixes and their application contexts are defined in the affix rule file referred by AffixFile configuration option See section 3 9 2 for details on affixation rules format If your language has ortographic accentuation such as Spanish Catalan and many other ro man languages the suffixation rules may have to deal with accent restoration when rebuilding the original roots To do this you have to to program a accents_mylanguage class derived from ab stract class accents_module which provides the service of restoring according to the accentuation rules in your langu
64. each step the selected pair is fused in a single chunk The process stops when only one chunk remains e The next step is an automatic conversion of the complete parse tree to a dependency tree Since the parsing grammar encodes information about the head of each rule the conversion is straighforward e The last step is the labeling Each edge in the dependeny tree is labeled with a syntactic function using the lt GRLAB gt rules The syntax and semantics of lt GRPAR gt and lt GRLAB gt rules are described in section 3 21 1 3 21 1 Dependency Parsing Rule File The dependency rules file contains a set of rules to perform dependency parsing The file consists of four sections sections lt GRPAR gt lt GRLAB gt lt SEMDB gt and lt CLASS gt respec tively closed by tags lt GRPAR gt lt GRLAB gt lt SEMDB gt and lt CLASS gt Parse tree completion rules Section lt GRPAR gt contains rules to complete the partial parsing provided by the chart parser The tree is completed by combining chunk pairs as stated by the rules Rules are applied from highest priority lower values to lowest priority higher values and left to right That is the pair of adjacent chunks matching the most prioritary rule is found and the rule is applied joining both chunks in one The process is repeated until only one chunk is left The rules can be enabled disabled via the activation of global flags Each rule may be stated to be enabled
65. el and lexicon files which may be generated from a training corpus using the scripts provided with FreeLing in folder src utilities nerc Check the README and comments in the scripts to find out what to do The most important file in the set is the rgf file which contains a definition of the context features that must be extracted for each named entity The feature rule language is described in section 3 24 The sections of the configuration file for bioner module are e Section lt RGF gt contains one line with the path to the RGF file of the model This file is the definition of the features that will be taken into account for NER These features are processed by libfries lt RGF gt ner rgf lt RGF gt e Section lt Classifier gt contains one line with the kind of classifier to use Valid values are AdaBoost and SVM lt Classifier gt Adaboost lt Classifier gt e Section lt ModelFile gt contains one line with the path to the model file to be used The model file must match the classifier type given in section lt Classifier gt lt ModelFile gt ner abm lt ModelFile gt The abm files contain AdaBoost models based on shallow Decision Trees see CMP03 for details You don t need to understand this unless you want to enter into the code of the AdaBoost classifier The svm files contain Support Vector Machine models generated by libsvm CL11 You don t need to understand this unless you want to enter int
66. elation encoded here is hypernymy Note also that semantic codes such as WN semantic file or EWN TopOntology features are simply lists of strings Thus you can include in this file any ontological or semantic information you need just substituing the WN related codes by your own semantic categories 3 24 Feature Extraction Module Machine Learning based modules such as BIO named entity recognition or classification modules require the encoding of each word to classify as a feature vector The conversion of words in a sentence to feature vectors is performed by this module The features are task oriented so they vary depending on what is being classified For this reason the encoding is not hard wired in the code but dinamically performed interpreting a set of feature rules Thus the Feature Extraction Module converts words in a sentence to feature vectors using a given set of rules The API of this module is the following class fex private public constructor given rule file lexicon file may be empty and custom functions fex const std wstring const std wstring const std map lt std wstring const feature_function gt 4 encode given sentence in features as feature names void encode_name const sentence amp std vector lt std set lt std wstring gt gt amp encode given sentence in features as integer feature codes void encode_int const sentence amp std vector lt std set lt int gt gt
67. er diverse licenses depending on their original sources Find the details in the COPYING file in the tarball or in the License section in FreeLing webpage 1 5 CONTRIBUTIONS 3 1 5 Contributions FreeLing is developed and maintained by people in TALP Research Center at Universitat Politec nica de Catalunya http talp upc edu Many people further contributed to by reporting problems suggesting various improvements submitting actual code or extending linguistic databases A detailed list can be found in Contributions section at FreeLing webpage http n1p 1si upc edu freeling CHAPTER 1 INTRODUCTION Chapter 2 Getting 1t to work 2 1 Requirements To install FreeLing you ll need e A typical Linux box with usual development tools bash make C compiler with basic STL support e Enough hard disk space 1 3 Gb for source and compilation files plus 600Mb for final installa tion e Some external libraries are required to compile FreeLing libboost amp libicu Boost library Included in all Linux distributions You probably do not have all neeeded components installed Make sure to install both runtime and development packages for libboost regex libicu libboost filesystem libboost program options x libboost locale only required for MacOSX or FreeBSD not needed in Linux Orientative package names check the package manager in your system Ubuntu Debian libboost dev libboost regex de
68. er line sentences separated with one blank line Each line has the format word lemma tag sense tokenized sentence splitted morphologically analyzed PoS tagged text and sense annotated One token per line sentences separated with one blank line e Output Format Command line Configuration file outf lt string gt OutputFormat lt string gt Format of output data token splitted morfo tagged shallow parsed dep token tokenized text one token per line splitted tokenized and sentence splitted text one token per line sentences separated with one blank line 74 CHAPTER 5 USING THE SAMPLE MAIN PROGRAM TO PROCESS CORPORA morfo tokenized sentence splitted and morphologically analyzed text One token per line sentences separated with one blank line Each line has the format word lemma tag prob or if sense tagging has been activated tagged tokenized sentence splitted morphologically analyzed and PoS tagged text One token per line sentences separated with one blank line Each line has the format word lemma tag prob shallow tokenized sentence splitted morphologically analyzed PoS tagged optionally sense annotated and shallow parsed text as output by the chart_parser module parsed tokenized sentence splitted morphologically analyzed PoS tagged optionally sense annotated and full parsed text as output by the first stage tree completion of
69. ers feature codes The constructor of the class receives a rgf file containing feature extraction rules a fea ture lexicon mapping feature names to integer codes and a map used to define custom feature functions with associations between feature_function objects and function names see section 3 24 5 If the lexicon file name is empty features will be assigned an integer code and the generated lexicon can be saved This is useful when encoding training corpus and feature codes have not been set yet 3 24 1 Feature Extraction Rule File Feature extraction rules are defined in a rgf file This section describes the format of the file The syntax of the rules is described in section 3 24 2 Rules are grouped in packages Begin and end of a package is marked with the keywords RULES and ENDRULES Packages are useful to simplify the rules and to speed up feature computation avoiding computing the same features several times The RULES package starting keyword must be followed by a condition see section 3 24 4 The rules in a package will onlly be applied to those words matching the package condition thus avoiding unnecessary tests For instance the rules in the package RULES t matches NP ENDRULES will be applied only for words with a PoS tag t starting with NP The same result could have been obtained without the package if the same condition was added to each rule but then applicability tests for each rule on each word would b
70. es Informatics Universitat Polit cnica de Catalunya February 1998 http www lsi upc edu padro W M Soon H T Ng and D C Y Lim A machine learning approach to coreference resolution of noun phrases Computational Linguistics 27 4 521 544 2001 Piek Vossen editor EuroWordNet A Multilingual Database with Lexical Semantic Networks Kluwer Academic Publishers Dordrecht 1998 87
71. es of each as you need Constructors for each of them receive the appropriate options e g the name of a dictionary hmm or grammar file so you can create each instance with the required capabilities for instance a tagger for English and another for Spanish 4 2 Sample programs The directory src main simple_examples in the tarball contains some example programs to illustrate how to call the library See the README file in that directory for details on what does each of the programs The most complete program in that directory is sample cc which is very similar to the program depicted below which reads text from stdin morphologically analyzes it and processes the obtained results Note that depending on the application the input text could be obtained from a speech recongnition system or from a XML parser or from any source suiting the application goals Similarly the obtained analysis instead of being output could be used in a translation system or sent to a dialogue control module etc int main wstring text list lt word gt lw list lt sentence gt ls set locale to an UTF8 compatible locale util init_locale L default if FreeLing was compiled with enable traces you can activate the required trace verbosity for the desired modules PGA traces TraceLevel 4 traces TraceModule 0xFFFFF instantiate analyzers as needed wstring path L usr local share freeling es
72. ext features that must be extracted for each named entity The feature rule language is described in section 3 24 The API of the class is the following class nec public Constructor nec const std string amp analyze given sentence void analyze sentence amp analyze given sentences void analyze std list lt sentence gt amp return analyzed copy of given sentence sentence analyze const sentence amp return analyzed copy of given sentences std list lt sentence gt analyze const std list lt sentence gt amp The constructor receives one parameter with the name of the configuration file for the module Its content is described below 3 19 1 NEC Data File The machine learning based Named Entity Classification module reads a configuration file with the following sections e Section lt RGF gt contains one line with the path to the RGF file of the model This file is the definition of the features that will be taken into account for NEC 44 CHAPTER 3 ANALYSIS MODULES lt RGF gt ner rgf lt RGF gt Section lt Classifier gt contains one line with the kind of classifier to use Valid values are AdaBoost and SVM lt Classifier gt Adaboost lt Classifier gt Section lt ModelFile gt contains one line with the path to the model file to be used The model file must match the classifier type given in section lt Classifier gt lt ModelFile gt ner abm lt ModelFi
73. fixed form No retokenization is performed The second rule les applies to all words and tries to check whether a valid verb form is obtained when removing the suffix les This is the case of words such as viles which may mean I saw them but also is the plural of the adjective vil In this case the retokenization info states that if eventually the verb tag is selected for this word it may be retokenized in two words The base verb form referred to as vi in the example plus the word les The tags for these new words are expressed after the colon The base form must keep its PoS tag this is what the second means and the second word may take any tag starting with PP it may have in the dictionary So for word viles we would obtain its adjective analysis from the dictionary plus its verb clitic pronoun from the suffix rule viles vil AQOCPO ver VMIS1SO The second analysis will carry the retokenization information so if eventually the PoS tagger selects the VMI analysis and the TaggerRetokenize option is set the word will be retokenized into vi ver VMIS1S0O les ellos PP3CPDOO 3 9 3 Dictionary Management In many NLP applications you want to deal with text in a particular domain which contain words or expressions terminology proper nouns etc that are specific or have a particular meaning in that domain Thus you may want to extend you dictionary to include such words There are two main ways of doing this e Extend
74. g into the code Since the text input language is an configuration option of the system a new configuration file must be created for the language to be added e g copying and modifying an existing one such as the example presented in section 5 4 2 6 1 Tokenizer The first module in the processing chain is the tokenizer As described in section 5 4 1 the behaviour of the tokenizer is controlled via the TokenizerFile option in configuration file To create a tokenizer for a new language just create a new tokenization rules file e g copying an existing one and adapting its regexps to particularities of your language and set it as the value for the TokenizerFile option in your new configuration file 6 2 Morphological analyzer The morphological analyzer module consists of several sub modules that may require language customization See sections 3 4 to 3 13 for details on data file formats for each module 6 2 1 Multiword detection The LocutionsFile option in configuration file must be set to the name of a file that contains the multiwords you want to detect in your language 6 2 2 Nummerical expression detection If no specialized module is defined to detect nummerical expressions the default behaviour is to recognize only numbers and codes written in digits or mixing digits and non digit characters If you want to recognize language dependent expressions such as numbers expressed in words e g one hundred thirthy six y
75. g make in src main simple_examples will compile all sample programs in that di rectory Make sure that the paths to FreeLing installation directory in Makefile are the right ones 66 CHAPTER 4 USING THE LIBRARY FROM YOUR OWN APPLICATION Chapter 5 Using the sample main program to process corpora The simplest way to use the FreeLing libraries is via the provided analyzer sample main program which allows the user to process an input text to obtain several linguistic processings Since it is impossible to write a program that fits everyone s needs the analyzer program offers you almost all functionalities included in FreeLing but if you want it to output more information or do so in a specific format or combine the modules in a different way the right path to follow is building your own main program or adapting one of the existing as described in section 4 2 The analyzer program is usually called with an option f config file if ommitted it will search for a file named analyzer cfg in the current directory The given config file must be an absolute file name or a relative path to the current directory You can use the default configuration files located at usr local share FreeLing config if you installed from tarball or at usr share FreeLing config if you used a deb package or either a config file that suits your needs Note that the default configuration files require the environment variable FREELINGSHARE to be defined and
76. g no alphanumeric character is considered a punctuation symbol This special line has the format lt Other gt tag E g lt Other gt Fz 3 7 User Map Module The user map module assigns Part of Speech tags to words matching a given regular expression It can be used to customize the behaviour of the analysis chain to specific applications or to process domain specific special tokens The API of the class is the following class RE_map public Constructor RE_map const std wstring amp analyze given sentence void analyze sentence 4 analyze given sentences void analyze std list lt sentence gt 4 return analyzed copy of given sentence sentence analyze const sentence 4 return analyzed copy of given sentences std list lt sentence gt analyze const std list lt sentence gt amp 3 The constructor receives as parameter the name of a file containing a list of regular expressions and the list of pairs lemma PoS tag to be assigned to each word matching the expression Note that this module will be applied afer the tokenizer so it will only annotate symbols that have been separated at the tokenization step So customizing your application to recognize certain special tokens will require modifying also the tokenizer configuration file Note also that if you introduce in this file PoS tags which are not in the tagset known to the tagger it may not be able to properly disambiguate the tag sequence
77. gt 3 2 Tokenizer Module The first module in the processing chain is the tokenizer It converts plain text to a vector of word objects according to a set of tokenization rules Tokenization rules are regular expressions that are matched against the beggining of the text line being processed The first matching rule is used to extract the token the matching substring is deleted from the line and the process is repeated until the line is empty 3 3 SPLITTER MODULE 11 The API of the tokenizer module is the following class tokenizer public Constructor receives the name of the file with tokenization rules tokenizer const std string amp tokenize string with default options std list lt word gt tokenize const std string amp tokenize string with default options accumulating byte offset of words std list lt word gt tokenize const std string amp unsigned long amp That is once created the tokenizer module receives plain text in a string tokenizes it and returns a list of word objects corresponding to the created tokens 3 2 1 Tokenizer Rules File The tokenizer rules file is divided in three sections lt Macros gt lt RegExps gt and lt Abbreviations gt Each section is closed by lt Macros gt lt RegExps gt and lt Abbreviations gt tags respectively The lt Macros gt section allows the user to define regexp macros that will be used later in the rules Macros are defined with a
78. h node contains a reference to a node in a parse_tree The structure of the dep_tree establishes syntactic dependency relationships between sentence constituents 4 1 2 Processing modules The main processing classes in the library are e tokenizer Receives plain text and returns a list of word objects 61 62 CHAPTER 4 USING THE LIBRARY FROM YOUR OWN APPLICATION e splitter Receives a list of word objects and returns a list of sentence objects e maco Receives a list of sentence objects and morphologically annotates each word ob ject in the given sentences Includes specific submodules e g detection of date number multiwords etc which can be activated at will e tagger Receives a list of sentence objects and disambiguates the PoS of each word object in the given sentences e nec Receives a list of sentence objects and modifies the tag for detected proper nouns to specify their class e g person place organitzation others e ukb_wrap Receives a list of sentence objects enriches the words with a ranked list of WordNet senses e parser Receives a list of sentence objects and associates to each of them a parse_tree object e dependency Receives a list of parsed sentence objects and associates to each of them a dep_tree object e coref Receives a document containing a list of parsed sentence objects and labels each noun phrase as belonging to a coreference group if appropriate You may create as many instanc
79. he order of the parameters is the same than the variables defined above void set_active_modules bool bool bool bool bool bool bool bool bool bool bool void set_data_files const std wstring amp const std wstring amp const std wstring amp const std wstring amp const std wstring amp const std wstring amp const std wstring amp const std wstring amp const std wstring amp void set_nummerical_points const std string amp const std string amp void set_threshold double void set_inverse_dict bool void set_retok_contractions bool To instantiate a Morphological Analyzer object the calling application needs to instantiate a maco_options object initialize its fields with the desired values and use it to call the constructor of the maco class The created object will create the required submodules and when asked to analyze some sentences it will just pass it down to each the submodule and return the final result 3 5 Number Detection Module The number detection module is language dependent It recognizes nummerical expression e g 1 220 54 or two hundred sixty five and assigns them a normalized value as lemma The module is basically a finite state automata that recognizes valid nummerical expressions Since the structure of the automata and the actions to compute the actual nummerical value are different for each lemma the automata is coded in C and has to be rewritten for any new language Fo
80. he sentence words The API of the parser is class chart_parser public Constructor chart_parser const std string amp Get the start symbol of the grammar std string get_start_symbol void const analyze given sentence void analyze sentence amp analyze given sentences void analyze std list lt sentence gt amp return analyzed copy of given sentence sentence analyze const sentence amp return analyzed copy of given sentences std list lt sentence gt analyze const std list lt sentence gt amp 3 The constructor receives a file with the CFG grammar to be used by the grammar which is described in the next section The method get_start_symbol returns the initial symbol of the grammar and is needed by the dependency parser see below 3 20 1 Shallow Parser CFG file This file contains a CFG grammar for the chart parser and some directives to control which chart edges are selected to build the final tree Comments may be introduced in the file starting with Z the comment will finish at the end of the line Grammar rules have the form x gt y A B That is the head of the rule is a non terminal specified at the left hand side of the arrow symbol The body of the rule is a sequence of terminals and nonterminals separated with commas and ended with a dot Empty rules are not allowed since they dramatically slow chart parsers Nevertheless any grammar may be written w
81. ictionaryFile path L maco db path L np dat path L common punct dat create the analyzer with the just build set of maco_options maco morfo opt create a hmm tagger for spanish with retokenization ability and forced to choose only one tag per word hmm tagger tagger L es path L tagger dat true true create chunker chart_parser parser path L grammar dep dat create dependency parser dep_txala dep path L dep dependences dat parser get_start_symbol Start text processing get plain text input lines while not EOF while getline wcin text tokenize input line into a list of words lw tk tokenize text accumulate list of words in splitter buffer returning a list of sentences The resulting list of sentences may be empty if the splitter has still not enough evidence to decide that a complete sentence has been found The list may contain more than one sentence since a single input line may consist of several complete sentences ls sp split lw false perform morphosyntactic analysis disambiguation and parsing morfo analyze ls tagger analyze ls parser analyze ls dep analyze ls Do application side processing with analysis results so far ProcessResults ls clear temporary lists lw clear Is clear No more lines to read Make sure the splitter doesn t retain anything s
82. ide whether an EAGLES tagset is being used and to appropriately shorten the tags if that s the case e The probabilities file name The file that contains all needed statistical information This file can be generated from a tagged training corpus using the scripts in src utilities Its format is described below e A threshold This is used for unknown words when the probability of each possible tag has been estimated by the guesser according to word endings tags with a value lower than this threshold are discarded 3 13 1 Lexical Probabilities File This file can be generated from a tagged corpus using the script src utilities train tagger bin TRAIN sh provided in FreeLing package See src utilities train tagger README find out how to use it The probabilities file has six sections lt UnknownTags gt lt Theeta gt lt Suffixes gt lt SingleTagFreq gt lt ClassTagFreq gt lt FormTagFreq gt lt BiassSuffixes gt lt LidstoneLambda gt Each section is closed by its corresponding tag lt UnknownTags gt lt Theeta gt lt Suffixes gt lt SingleTagFreq gt lt ClassTagFreq gt lt FormTagFreq gt lt BiassSuffixes gt lt LidstoneLambda gt e Section lt FormTagFreq gt Probability data of some high frequency forms If the word is found in this list lexical probabilities are computed using data in lt FormTagFreq gt section 3 13 PROBABILITY ASSIGNMENT AND UNKOWN WORD GUESSER MODULE 31 The list consi
83. ion If no specialized module is defined to detect date time expressions the default behaviour is to recognize only simple percentage expressions such as 23 If you want to recognize language dependent expressions such as complex ratio expressions e g three out of four or currency expression e g 2 000 australian dollar you have to program a quantities_mylanguage class derived from abstract class quantities_module Those classes are finite automata that recognize word sequences An abstract class automat controls the sequence advance so your derived class has little work to do apart from defining states and transitions for the automaton A good idea to start with this issue is having a look at the quantities_es and quantities_ca classes In the case your language is a roman language or at least has a similar structure for currency expressions you can easily develop your currency expression detector by copying the quantities_es class and modifying the CurrencyFile option to provide a file in which lexical items are adapted to your language For instance Catalan currency recognizer uses a copy of the quantities_es class but a different CurrencyFile since the syntactical structure for currency expression is the same in both languages but lexical forms are different If your language has a very different structure for those expressions you may require a different format for the CurrencyFile contents Since that fil
84. ion Module The punctuation detection module assigns Part of Speech tags to punctuation symbols The API of the class is the following class punts public Constructor receives data file name punts const std string amp analyze given sentence void analyze sentence 4 analyze given sentences void analyze std list lt sentence gt amp return analyzed copy of given sentence sentence analyze const sentence amp return analyzed copy of given sentences std list lt sentence gt analyze const std list lt sentence gt amp 3 The constructor receives as parameter the name of a file containing the list of the PoS tags to be assigned to each punctuation symbol Note that this module will be applied afer the tokenizer so it will only annotate symbols that have been separated at the tokenization step For instance if you include the three suspensive dots as a single punctuation symbol it will have no effect unless the tokenizer has a rule that causes these substring to be tokenized in one piece 3 6 1 Punctuation Tags File The format of the file listing the PoS for punctuation symbols is one punctuation symbol per line each line with the format punctuation symbol lemma tag E g 16 CHAPTER 3 ANALYSIS MODULES Fat Ea Pe io Fd Fs One special line may be included defining the tag that will be assigned to any other punctuation symbol not found in the list Any token containin
85. is for instance VMIP3S0 lt mylemmas dat gt or VMI myforms dat it is interpreted as a file name and the terminal will match any lemma or word form found in that file If the file name is not an absolute path it is interpreted as a relative path based at the location of the grammar file The grammar file may contain also some directives to help the parser decide which chart edges must be selected to build the tree Directive commands start with the directive name always prefixed with Q followed by one or more non terminal symbols separated with spaces The list must end with a dot e NOTOP Non terminal symbols listed under this directive will not be considered as valid tree roots even if they cover the complete sentence e START Specify which is the start symbol of the grammar Exactly one non terminal must be specified under this directive The parser will attempt to build a tree with this symbol as a root If the result of the parsing is not a complete tree or no valid root nodes are found a fictitious root node is created with this label and all created trees are attached to it e FLAT Subtrees for Hat non terminal symbols are flattened when the symbol is recursive Only the highest occurrence appears in the final parse tree e HIDDEN Non teminal symbols specified under this directive will not appear in the final parse tree their descendant nodes will be attached to their parent e PRIOR lists of non termin
86. ithout empty rules assuming you are not going to accept empty sentences Rules with the same head may be or ed using the bar symbol as in x gt A y B C The head component for the rule maybe specified prefixing it with a plus sign e g nounphrase gt DT ADJ N prepphrase If the head is not specified the first symbol on the right hand side is assumed to be the head The head marks are not used in the chart parsing module but are necessary for later dependency tree building The grammar is case sensitive so make sure to write your terminals PoS tags exactly as they are output by the tagger Also make sure that you capitalize your non terminals in the same way everywhere they appear Terminals are PoS tags but some variations are allowed for flexibility e Plain tag A terminal may be a plain complete PoS tag e g VMIP3S0 e Wildcarding A terminal may be a PoS tag prefix right wilcarded e g VMIx VMIP e Specifying lemma A terminal may be a PoS tag or a wilcarded prefix with a lemma enclosed in angle brackets e g VMIP3S0 lt comer gt VMI lt comer gt will match only words with those tag prefix and lemma 46 CHAPTER 3 ANALYSIS MODULES e Specifying form A terminal may be a PoS tag or a wilcarded prefix with a form enclosed in parenthesis e g VMIP3S0 comi VMI comi will match only words with those tag prefix and form e If a double quoted string is given inside the angle brackets or parenthes
87. le gt The abm files contain AdaBoost models based on shallow Decision Trees see CMP03 for details You don t need to understand this unless you want to enter into the code of the AdaBoost classifier The svm files contain Support Vector Machine models generated by libsvm CL11 You don t need to understand this unless you want to enter into the code of libsvm Section lt Lexicon gt contains one line with the path to the lexicon file of the learnt model The lexicon is used to translate string encoded features generated by libfries to integer encoded features needed by libomlet The lexicon file is generated by libfries at training time lt Lexicon gt ner lex lt Lexicon gt The lex file is a dictionary that assigns a number to each symbolic feature used in the AdaBoost or SVM model You don t need to understand this either unless you are a Machine Learning student or the like Section lt Classes gt contains only one line with the classes of the model and its translation to B I O tag lt Classes gt O NPOOSPO 1 NPOOGOO 2 NPO0000 3 NPOOVOO lt Classes gt Section lt NE_Tag gt contains only one line with the PoS tag assigned by the NER module which will be used to select named entities to be classified lt NE_Tag gt NP00000 lt NE_Tag gt 3 20 CHART PARSER MODULE 45 3 20 Chart Parser Module The chart parser enriches each sentence object with a parse_tree object whose leaves have a link to t
88. llowed by 1 m any character in category V and the end of the word Rule U L C_CV replaces any character in category U with the character in the same position in category L when preceeded by any character in category C and followed by any character in category C and any character in category V Note that uppercase characters for categories is just a convention An uppercase letter may be a terminal symbol and a lowercase may be a category name Non alphabetical characters are also allowed If a character is not defined as a category name it will be considered a terminal character 3 19 Named Entity Classification Module The mission of the Named Entity Classification module is to assing a class to named entities in the text It is a Machine Learning based module so the classes can be anything the model has been trained to recognize When classified the PoS tag of the word is changed to the label defined in the model This module depends on a NER module being applied previously If no entities are recognized none can be classified Models provided with FreeLing distinguish four classes Person tag NPOOSPO Geographical location NPOOGOO Organization NP00000 and Others NPOOVOO If you have an anotated corpus the models can be trained using the scripts in src utilities nerc See the README there and the comments inside the script for details The most important file in the set is the rgf file which contains a definition of the cont
89. mbiguator Configuration File described below Sense Annotation Configuration File Command line Configuration file W lt filename gt fsense lt filename gt SenseConfigFile lt filename gt Word sense annotator configuration file See section 3 15 for details UKB Word Sense Disambiguator Configuration File Command line Configuration file U lt filename gt fukb lt filename gt UKBConfigFile lt filename gt UKB configuration file See section 3 16 for details Tagger algorithm Command line Configuration file t lt string gt tag lt string gt Tagger lt string gt Algorithm to use for PoS tagging hmm Hidden Markov Model tagger based on Bra00 relax Relaxation Labelling tagger based on Pad98 HMM Tagger configuration File Command line Configuration file H lt filename gt hmm lt filename gt TaggerHMMFile lt filename gt Parameters file for HMM tagger See section 3 17 1 for details Relaxation labelling tagger constraints file Command line Configuration file R lt filename gt rlx lt filename gt TaggerRelaxFile lt filename gt File containing the constraints to apply to solve the PoS tagging See section 3 17 2 for details Relaxation labelling tagger iteration limit Command line Configuration file i lt int gt iter lt int gt TaggerRelaxMaxIter lt int gt Maxi
90. mum numbers of iterations to perform in case relaxation does not converge Relaxation labelling tagger scale factor Command line Configuration file r lt float gt sf lt float gt TaggerRelaxScaleFactor lt float gt Scale factor to normalize supports inside RL algorithm It is comparable to the step lenght in a hill climbing algorithm The larger scale factor the smaller step 5 4 CONFIGURATION FILE AND COMMAND LINE OPTIONS 79 e Relaxation labelling tagger epsilon value Command line Configuration file eps lt float gt TaggerRelaxEpsilon lt float gt Real value used to determine when a relaxation labelling iteration has produced no significant changes The algorithm stops when no weight has changed above the specified epsilon e Retokenize contractions in dictionary Command line Configuration file rtkcon nortkcon RetokContractions yeslylonlno nlof f Specifies whether the dictionary must retokenize contractions when found or leave the de cision to the TaggerRetokenize option Note that if this option is active contractions will be retokenized even if the TaggerRetokenize option is not active If this option is not active contractions will be retokenized depending on the value of the TaggerRetokenize option e Retokenize after tagging Command line Configuration file rtk nortk TaggerRetokenize yes y on no n off Determine whether
91. n of language specific finite state automata and for this reason needs no data file to be provided at instantiation time For languages that do not have a specific automata a default analyzer is used that detects simple date patterns e g DD MM AAAA MM DD AAAA etc The API of the class is class dates public Constructor receives the language code dates const std string amp analyze given sentence void analyze sentence 4 analyze given sentences void analyze std list lt sentence gt amp return analyzed copy of given sentence sentence analyze const sentence 4 return analyzed copy of given sentences std list lt sentence gt analyze const std list lt sentence gt amp 3 The only parameter expected by the constructor is the language of the text to analyze in order to be able to apply the appropriate specific automata or select the default one if none is available 3 9 Dictionary Search Module The dictionary search module has two functions Search the word forms in the dictionary to find out their lemmas and PoS tags and apply affixation rules to find the same information in the cases in which the form is a derived form not included in the dictionary e g the word quickly may not be in the dictionary but a suffixation rule may state that removing ly and searching for the obtained adjective is a valid way to form and adverb The decision of what is included in the dictionary and
92. n system an intelligent indexation module for a search engine etc To achieve this goal you have to link your application to the library and access it via the provided API Currently the library provides a complete C API a quite complete Java API and half complete perl and python APIs 4 1 Basic Classes This section briefs the basic C classes any application needs to know For detailed API defini tion consult the technical documentation in doc html and doc latex directories 4 1 1 Linguistic Data Classes The different processing modules work on objects containing linguistic data such as a word a PoS tag a sentence Your application must be aware of those classes in order to be able to provide to each processing module the right data and to correctly interpret the module results The Linguistic Data classes are defined in libfries library Refer to the documentation in that library for the details on the classes The linguistic classes are e analysis A tuple lt lemma PoS tag probability sense list gt e word A word form with a list of possible analysis e sentence A list of words known to be a complete sentence A sentence may have associated a parse_tree object and a dependency_tree e parse_tree An n ary tree where each node contains either a non terminal label or if the node is a leaf a pointer to the appropriate word object in the sentence the tree belongs to e dep tree An n ary tree where eac
93. n ucl ac uk home sampa 42 CHAPTER 3 ANALYSIS MODULES analyze sentences return analyzed copy std list lt sentence gt analyze const std list lt sentence gt amp The constructor receives a configuration file that contains the transformation rules to apply and the exception dictionary The module can be used to transform a single string method get_sound or to enrich all words in a sentence or sentence list with their phonetic information The phonetic encoding is stored in the word object and can be retrieved with the method word get_ph_form The format of the configuration file is the following There can be at most one exception dictionary delimited by lt Exceptions gt and lt Exceptions gt Each entry in the dictionary contains two fields a lowercase word form and the output phonetic encoding E g addition dISIn varieties B ralItiz worcester wUst0Or If a word form is found in the exceptions dictionary the corresponding phonetic string is returned and no transformation rules are applied There can be one or more rulesets delimited by lt Rules gt and lt Rules gt Rulesets are applied in the order they are defined Each ruleset is applied on the result of the previous Rulesets can contain two kind of lines Category definitions and rules Category definitions are of the form X abcde and define a set of characters under a single name Category names must have exactly one character which should not b
94. name and a POSIX regexp E g MYALPHA A Za z ALPHA alpha The lt RegExps gt section defines the tokenization rules Previously defined macros may be referred to with their name in curly brackets E g ABREVIATIONS1 O ALPHA 7 Rules are regular expressions and are applied in the order of definition The first rule matching the beginning of the line is applied a token is built and the rest of the rules are ignored The process is repeated until the line has been completely processed The format of each rule is e The first field in the rule is the rule name If it starts with a the RegExp will only produce a token if the match is found in the abbreviation list lt Abbreviations gt section Apart from that the rule name is only for informative readability purposes e The second field in the rule is the substring to form the token s with It may be 0 the match of the whole expression or any number from 1 to the number of subexpression up to 9 A token will be created for each subexpression from 1 to the specified value e The third field is the regexp to match against the input line Any POSIX regexp convention may be used e An optional fourth field may be added containing the string CI standing for Case Insensi tive In this case the input text will be matched case insensitively against the regexp If the fourth field is not present or it is different than CI the rule is matched case sensitively
95. nction labeling rules Section lt GRLAB gt contains two kind of lines The first kind are the lines defining UNIQUE labels which have the format UNIQUE label1 label2 label3 You can specify many UNIQUE lines each with one or more labels The effect is the same than having all of them in a single line and the order is not relevant Labels in UNIQUE lists will be assigned only once per head That is if a head has a daugther with a dependency already labeled as label rules assigning this label will be ignored for all other 50 CHAPTER 3 ANALYSIS MODULES daugthers of the same head e g if a verb has got a subject label for one of its dependencies no other dependency will get that label even if it meets the conditions to do so The second kind of lines state the rules to label the dependences extracted from the full parse tree build with the rules in previous section Each line contains a rule with the format ancestor label dependence label conditioni condition2 where e ancestor label is the label of the node which is head of the dependence e dependence label is the label to be assigned to the dependence e condition 19 a list of conditions that the dependence has to match to satisfy the rule Each condition has one of the forms node attribute value node attribute value Where node is a string describing a node on which the attribute has to be checked The value is a string to be matched or a set of strings se
96. ntence 4 3 11 NAMED ENTITY RECOGNITION MODULE 23 return analyzed copy of given sentences std list lt sentence gt analyze const std list lt sentence gt 4 The file that controls the behaviour of the simple NE recognizer consists of the following sections e Section lt FunctionWords gt lists the function words that can be embeeded inside a proper noun e g preposisions and articles such as those in Banco de Espa a or Foundation for the Eradication of Poverty For instance lt FunctionWords gt el la los las de del para lt FunctionWords gt e Section lt SpecialPunct gt lists the PoS tags according to punctuation tags definition file section 3 6 after which a capitalized word may be indicating just a sentence or clause beggining and not necessarily a named entity Typical cases are colon open parenthesis dot hyphen lt SpecialPunct gt Fpa Fp Fd Fg lt SpecialPunct gt e Section lt NE_Tag gt contains only one line with the PoS tag that will be assigned to the recognized entities If the NE classifier is going to be used later it will have to be informed of this tag at creation time lt NE_Tag gt NP00000 lt NE_Tag gt e Section lt Ignore gt contains a list of forms lowercased or PoS tags uppercased that are not to be considered a named entity even when they appear capitalized in the middle of a sentence For instance the word Spanish in the sentence He
97. ntities const std string amp const std string amp Detect magnitude expressions starting at given sentence position bool matching sentence amp sentence iterator amp analyze given sentence void analyze sentence amp analyze given sentences void analyze std list lt sentence gt amp return analyzed copy of given sentence sentence analyze const sentence amp return analyzed copy of given sentences std list lt sentence gt analyze const std list lt sentence gt amp 3 3 12 1 Quantity Recognition Data File This file contains the data necessary to perform currency amount and physical magnitude recog nition It consists of three sections lt Currency gt lt Measure gt and lt MeasureNames gt 3 12 QUANTITY RECOGNITION MODULE 29 Section lt Currency gt contains a single line indicating which is the code among those used in section lt Measure gt that stands for currency amount This is used to assign to currency ammounts a different PoS tag than physical magnitudes E g lt Currency gt CUR lt Currency gt Section lt Measure gt indicates the type of measure corresponding to each possible unit Each line contains two fields the measure code and the unit code The codes may be anything at user s choice and will be used to build the lemma of the recognized quantity multiword E g the following section states that USD and FRF are of type CUR currency
98. o the code of libsvm e Section lt Lexicon gt contains one line with the path to the lexicon file of the learnt model The lexicon is used to translate string encoded features generated by libfries to integer encoded features needed by libomlet The lexicon file is generated by libfries at training time lt Lexicon gt ner lex lt Lexicon gt The lex file is a dictionary that assigns a number to each symbolic feature used in the AdaBoost or SVM model You don t need to understand this either unless you are a Machine Learning student or the like e Section lt UseSoftMax gt contains only one line with yes or no indicating whether the classifier output must be converted to probabilities with the SoftMax function Currently AdaBoost models need that conversion and SVM models do not lt UseSoftMax gt yes lt UseSoftMax gt 3 11 NAMED ENTITY RECOGNITION MODULE 27 e Section lt Classes gt contains only one line with the classes of the model and its translation to B I O tag lt Classes gt 0B 1 TL 2 0 lt Classes gt Section lt NE_Tag gt contains only one line with the PoS tag that will be assigned to the recognized entities If the NE classifier is going to be used later it will have to be informed of this tag at creation time lt NE_Tag gt NP00000 lt NE_Tag gt Section lt InitialProb gt Contains the probabilities of seeing each class at the begining of a sentence These probabilities are necessa
99. oS n a r and v respectively It also states that words with tag starting with VMP e g cansadas must be searched as adjectives a using the form for the same lema i e cansar that matches the tag VMPOOSM resulting in cansado This is useful to have participles searched as adjectives since FreeLing Spanish dictionary doesn t contain any participle as adjective but es WN does 3 23 2 Sense Dictionary File This source file e g senses30 src provided with FreeLing must contain the word list for each synset one entry per line Each line has format sense word1 word2 E g 00045250 n actuation propulsion 3 24 FEATURE EXTRACTION MODULE 55 00050652 v assume don get_into put_on wear Sense codes can be anything assuming your later processes know what to do with them provided they are unambiguous there are not two lines with the same sense code The files provided in FreeLing contain WordNet 3 0 synset codes 3 23 3 WordNet file This source file e g wn30 src provided with FreeLing must contain at each line the information relative to a sense with the following format sense hypern hypern hypern semfile TopOnto TopUnto TopUnto That is the first field is the sense code The second field is a colon separated list of its hypernym senses The third field is the WN semantic file the synset belongs to and the last field is a colon separated list of EuroWN TopOntology codes valid for the synset Note that the only r
100. oS tag which is assigned to any quote symbol by the punctuation module class fquoted public feature_function public void extract const sentence amp sent int i std list lt std wstring gt amp res const 4 if i gt 0 and sent i 1 get_tag L Fe and i lt Gint sent size 1 and sent i 1 get_tag L Fe res push_back L in_quotes We can associate this function with the function name quoted adding the pair to a map map lt wstring const feature_function gt myfunctions myfunctions insert make_pair L quoted feature_function new fquoted 60 CHAPTER 3 ANALYSIS MODULES If we now create a fex object passing this map to the constructor the created instance will call fquoted extract with the appropriate parameters whenever a rule in the rgf file refers to e g quote 0 Note that there are three naming levels for custom feature functions e The name of the feature itself which will be generated by the extractor and will appear in the feature vectors in_quotes in the above example e The name of the function that will be called from the extraction rules in the rgf file quoted in the above example e The name of the class derived from feature_function that has a method extract which actually computes the feature fquoted in the above example Chapter 4 Using the library from your own application The library may be used to develop your own NLP application e g a machine translatio
101. oding WN sense annotation UKB sense disambiguation Shallow parsing X Full dependency parsing X Coreference resolution Maa gt bd Kal 9 bd bd bd bd dd bd dd Ddl S bd bd bd gt bd al bd bd bd bd bed bd bdl gt d E AP I A PA OO PS KM MMM MMM MM pA d d dS KM PS PS PS MMM Pd pd MM Pd Dd pd DS Pd lS mrss a Add Table 1 1 Analysis services available for each language See the Linguistic Data section on FreeLing webpage to find out about the size of the morpho logical dictionaries and the source of their data FreeLing also includes WordNet based sense dictionaries for some of the covered languages as well as some knowledge extracted from WordNet such as semantic file codes or hypernymy relationships e The English sense dictionary is straightforwardly extracted from WN 3 0 and therefore is distributed under the terms of WN license as all knowledge extracted from WN contained in this package e Spanish sense dictionary is a reduced subset extracted from EuroWordNet This subset included in this FreeLing package is distributed under LGPL e Catalan sense dictionary is extracted from CREL project funded by the Catalan government and is distributed under GPL See http wordnet princeton edu for details on WordNet and http ww illc uva nl EuroWordNet for more information on EuroWordNet 1 4 License FreeLing code is licensed under GNU General Public License GPL The linguistic data are licensed und
102. ommand Line Options Almost all options may be specified either in the configuration file or in the command line having the later precedence over the former Valid options are presented in section 5 4 1 both in their command line and configuration file notations Configuration files follow the usual linux standards A sample file may be seen in section 5 4 2 The FreeLing package includes default configuration files They can be found at the directory share FreeLing config under the FreeLing installation directory usr local if you installed from source and usr share FreeLing if you used a binary deb package The analyze script will try to locate the configuration file in that directory if it is not found in the current working directory 5 4 1 Valid options This section presents the options that can be given to the analyzer program and thus also to the analyzer_server program and to the analyze script All options can be written in the configuration file as well as in the command line The later has always precedence over the former 5 4 CONFIGURATION FILE AND COMMAND LINE OPTIONS 71 e Help Command line Configuration file h help help cf N A Prints to stdout a help screen with valid options and exits help provides information about command line options help cf provides information about configuration file options e Version number Command line Configuration file v version N A
103. on no n off Whether to perform coreference resolution 80 CHAPTER 5 USING THE SAMPLE MAIN PROGRAM TO PROCESS CORPORA e Named Entity Classifier File Command line Configuration file C lt filename gt fcorf lt filename gt CorefFile lt filename gt Configuration file for coreference resolution module See section 3 22 for details 5 4 CONFIGURATION FILE AND COMMAND LINE OPTIONS 81 5 4 2 Sample Configuration File A sample configuration file follows This is only a sample and probably won t work if you use it as is You can start using freeling with the default configuration files which after installation are located in usr local share FreeLing config note than prefix usr local may differ if you specified an alternative location when installing FreeLing If you installed from a binary deb package it will be at usr share FreeLing config You can use those files as a starting point to customize one configuration file to suit your needs Note that file paths in the sample configuration file contain FREELINGSHARE which is supposed to be an environment variable If this variable is not defined the analyzer will abort complaining about not finding the files If you use the analyze script it will define the variable for you as usr local share Freeling or the right installation path unless you define it to point somewhere else You can also adjust your configuration files to use normal p
104. only if certain flags are on If none of its enabling flags are on the rule is not applied Each rule may also state which flags have to be toggled on off after its application thus enabling disabling other rule subsets Each line contains a rule with the format priority flags context ancestor descendant operation op params flag ops where e priority is a number stating the priority of a rule the lower the number the higher the priority e flags is a list of strings separated by vertical bars Each string is the name of a flag that will cause the rule to be enabled If enabling_flags equals the rule will be always enabled e context is a context limiting the application of the rule only to chunk pairs that are sur rounded by the appropriate context means no limitations and the rule is applied to any matching chunk pair see below e ancestor descendant are the labels of the adjacent pair of chunks the rule will be applied to The labels are either assigned by the chunk parser or by a RELABEL operation on some other completion rule The pair must be enclosed in parenthesis separated by a comma and contain NO whitespaces The chunk labels may be suffixed with one extra condition of the form form lt lemma gt class or PoS_regex For instance 48 CHAPTER 3 ANALYSIS MODULES The label Would match np any chunk labeled np by the chunker np cats any chunk labeled np by the ch
105. ons the label following the key word MATCHING states the label that a node must have in order to be considered a valid last and get the subtree as a new child This label may carry the same modifying suffixes than the chunk labels If no node with this label is found in the tree the rule is not applied For example the rule 20 vp pp lt of gt last_left MATCHING np will hang the pp chunk as a daughter of the last subtree labeled np found inside the vp chunk e The last field flag ops is a space separated list of flags to be toggled on off The list may be empty meaning that the rule doesn t change the status of any flag If a flag name is preceded by a it will be toggled on If the leading symbol is a it will be toggled off For instance the rule 20 np pp lt of gt top_left RELABEL states that if two subtrees labelled np and pp are found contiguous in the partial tree and the second head word has lemma of then the later rightmost is added as a new child of the former leftmost whatever the context is without need of any special flag active and performing no relabelling of the new tree root The supported tree building operations are the following e top_left The right subtree is added as a daughter of the left subtree The root of the new tree is the root of the left subtree If a label value other than is specified the root is relabelled with that string 3 21 DEPENDE
106. onsidered as possible sentence endings Each character is followed by a binary value stating whether the character is an unambiguous sentence ending or not For instance in the following example is an unambiguous sentence marker so a sentence split will be introduced unconditionally after each The other two 3 4 MORPHOLOGICAL ANALYZER MODULE 13 characters are not unambiguous so a sentence split will only be introduced if they are followed by a capitalized word or a sentence start character lt SentenceEnd gt 0 1 0 lt SentenceEnd gt The lt SentenceStart gt section lists characters known to appear only at sentence beggining For instance open question exclamation marks in Spanish lt SentenceStart gt i lt SentenceStart gt 3 4 Morphological Analyzer Module The morphological analyzer is a meta module which does not perform any processing of its own It is just a convenience module to simplify the instantiation and call to the submodules de scribed in the next sections from 3 5 to 3 13 At instantiation time it receives a maco_options object containing information about which submodules have to be created and which files have to be used to create them A calling application may bypass this module and just call directly the submodules The Morphological Analyzer API is class maco public Constructor Receives a set of options maco const maco_options amp analyze given senten
107. operties will be satisfied if any of the word senses matches the condition For instance the rule verb phr subj d label np d side left 3 21 DEPENDENCY PARSER MODULE 51 states that if a verb phr node has a daughter to its left with a label starting by np this dependence is to be labeled as subj Similarly the rule verb phr obj d label np d sn tonto Edible p lemma eat gulp states that if a verb phr node has eat or gulp as lemma and a descendant with a label starting by np and containing a daughter labeled sn that has Edible property in EWN Top ontology this dependence is to be labeled as obj Another example verb phr iobj d label pp d lemma to for Es label np states that if a verb phr has a descendant with a label starting by pp prepositional phrase and lemma to or for and there is another child of the same parent which is a noun phrase np this dependence is to be labeled as iobj Yet another verb phr dobj d label pp d lemma to for As label np states that if a verb phr has a descendant with a label starting by pp prepositional phrase and lemma to or for and all the other children of the same parent are not noun phrases np this dependence is to be labeled as dobj Semantic database location Section lt SEMDB gt is only necessary if the dependency labeling rules in section lt GRLAB gt use condi tions on semantic values that is any of tonto semfile synon or asynon Since it is needed by lt
108. or your language please contact FreeLing authors Section lt SemDB gt specifies the files that contain a semantic database This is required to compute some WN based attributes used by the solver The section must contain two lines specifying two semantic information files a SenseFile and a WNFile The filenames may be absolute or relative to the location of the dependency rules file For example lt SEMDB gt SenseFile senses30 src WNFile common wn30 src lt SEMDB gt 3 23 SEMANTIC DATABASE MODULE 53 e Section lt MaxDistance gt states the maximum distance in words at which possible corefer neces will be considered Short values will cause the solver to miss distant coreferents Long distances will introduce a huge amount of possible coreferent candidate pairs slow the sys tem and produce a larger amount of false positives 3 23 Semantic Database Module This module is not a main processor in the default analysis chain but it is used by the other modules that need access to the semantic database The sense annotator senses the word sense disambiguator ukb_wrap the dependency parser dep_txala and the coreference solver coref Moreover this module can be used by the applications to enrich or post process the results of the analysis The API for this module is class semanticDB public Constructor semanticDB const std string amp Compute list of lemma pos to search in WN for given word
109. ou have to program a numbers_mylanguage class derived from abstract class numbers_module Those classes are finite automata that recognize word sequences An abstract class automat controls the sequence advance so your derived class has little work to do apart from defining states and transitions for the automaton 83 84CHAPTER 6 EXTENDING THE LIBRARY WITH ANALYZERS FOR NEW LANGUAGES A good idea to start with this issue is having a look at the numbers_es numbers_en and numbers_ca classes State transition diagrams of those automata can be found in the directory doc diagrams 6 2 3 Date time expression detection If no specialized module is defined to detect date time expressions the default behaviour is to recognize only simple date expressions such as DD MM YYYY If you want to recognize language dependent expressions such as complex time expressions e g wednesday July 12th at half past nine you have to program a date_mylanguage class derived from abstract class dates_module Those classes are finite automata that recognize word sequences An abstract class automat controls the sequence advance so your derived class has little work to do apart from defining states and transitions for the automaton A good idea to start with this issue is having a look at the dates_es dates_en and dates_ca classes State transition diagrams of those automata can be found in the directory doc diagrams 6 2 4 Currency ratio expression detect
110. p split lw true ls analyze sentence s which might be lingering in the buffer if any morfo analyze ls tagger analyze ls 64 CHAPTER 4 USING THE LIBRARY FROM YOUR OWN APPLICATION parser analyze ls dep analyze ls process remaining sentences if any ProcessResults ls The processing performed on the obtained results would obviously depend on the goal of the application translation indexation etc In order to illustrate the structure of the linguistic data objects a simple procedure is presented below in which the processing consists of merely printing the results to stdout in XML format void ProcessResults const list lt sentence gt amp ls list lt sentence gt const_iterator is word const_iterator a iterator over all analysis of a word sentence const_iterator w for each sentence in list for is ls begin is ls end is weout lt lt L lt SENT gt lt lt endl for each word in sentence for w is gt begin w l is gt end w print word form with PoS and lemma chosen by the tagger wcout lt lt T lt WORD form lt lt w gt get_form wcout lt lt L lemma lt lt w gt get_lemma weout lt lt L pos lt lt w gt get_tag wcout lt lt L Y gt lt lt endl for each possible analysis in word output lemma tag and probability for a w gt analysis_begin al w gt analysis_end
111. parated by The strings can be right wildcarded e g np is allowed but not n p For the pos attribute value can be any valid regular expression The node expresses a path to locate the node to be checked The path must start with p parent node or d descendant node and may be followed by a colon separated list of labels For instance p sn n refers to the first node labeled n found under a node labeled sn which is under the dependency parent p The node may be also As All siblings or Es Exists sibling which will check the list of all children of the ancestor p excluding the focus daughter d As and Es may be followed by a path just like p and d For instance Es sn n will check for a sibling with that path and As sn n will check that all sibling have that path Possible attribute to be used e label chunk label or PoS tag of the node e side left or right position of the specified node with respect to the other Only valid for p and d e lemma lemma of the node head word e pos PoS tag of the node head word e class word class see below of lemma of the node head word e tonto EWN Top Ontology properties of the node head word e semfile WN semantic file of the node head word e synon Synonym lemmas of the node head word according to WN e asynon Synonym lemmas of the node head word ancestors according to WN Note that since no disambiguation is required the attributes dealing with semantic pr
112. plitter File Command line Configuration file fsplit lt filename gt SplitterFile lt filename gt File of splitter options rules See section 3 3 for details 5 4 CONFIGURATION FILE AND COMMAND LINE OPTIONS 75 e Affix Analysis Command line Configuration file afx noafx AffixAnalysis yesly on nol n off Whether to perform affix analysis on unknown words Affix analysis applies a set of affixation rules to the word to check whether it is a derived form of a known word e Affixation Rules File Command line Configuration file S lt filename gt fafx lt filename gt AffixFile lt filename gt Affix rules file See section 3 9 2 for details e User Map Command line Configuration file usr nousr UserMap yeslylonlnolnloff Whether to apply or not a file of customized word tag mappings e User Map File Command line Configuration file M lt filename gt fmap lt filename gt UserMapFile lt filename gt User Map file to be used See section 3 7 for details e Multiword Detection Command line Configuration file loc noloc MultiwordsDetection yes y on no n off Whether to perform multiword detection This option requires that a multiword file is provided e Multiword File Command line Configuration file L lt filename gt floc lt filename gt LocutionsFile lt filename gt
113. quire it It is a regular form file with morphological information as described in section 3 9 3 23 1 PoS mapping rules Each line in section lt WNPosMap gt defines a mapping rule with the format FreeLing PoS WN PoS search key where FreeLing PoS is a prefix for a FreeLing PoS tag WN Pos must be one of n a r or v and search key defines what should be used as a lemma to search the word in WN files The given search key may be one of L F or a FreeLing PoS tag If L F is given the word lemma form will be searched in WN to find candidate senses If a FreeLing PoS tag is given the form for that lemma with the given tag will be used Example 1 For English we could have a mapping like lt WNposMap gt N nL J aL RrL Y Y L VBG a F lt WNposMap gt which states that for words with FreeLing tags starting with N J R and V lemma will be searched in wordnet with PoS n a r and v respectively It also states that words with tag VBG e g boring must be searched as adjectives a using their form that is boring instead of lemma bore This may be useful for instance if FreeLing English dictionary assigns to that form a gerund analysis bore VBG but not an adjective one Example 2 A similar example for Spanish could be lt WNposMap gt Nal A aL R r L Y Y L VMP a VMPOOSM lt WNposMap gt which states that for words with FreeLing tags starting with N A R and V lemma will be searched in wordnet with P
114. r E g lt BiassSuffixes gt 0 4 lt BiassSuffixes gt Default value is 0 3 The probability of the tags belonging to words unobserved in the training corpus is computed backing off to the distribution of all words with the same ambiguity class This obviously overgeneralizes and for some words the estimated probabilities may be rather far from reality To palliate this overgeneralization the ambiguity class probabilities can me interpolated with the probabilities assigned by the guesser according to the word suffix This parameter specifies the weight that suffix information is given in the iterpolation i e if BiassSuffixes 0 only the ambiguity class information is used If BiassSuffixes 1 only the probabilities provided by the guesser are used 32 CHAPTER 3 ANALYSIS MODULES e Section lt Suffixes gt List of suffixes obtained from a train corpus with information about which tags were assigned to the word with that suffix The list has one suffix per line each line with format suffix observ tagl ttobservl tag2 observ2 E g orada 133 AQOFSP 17 VMPOOSF 8 NCFSOOO 108 e Section lt UnknownTags gt List of open category tags to consider as possible candidates for any unknown word One tag per line each line with format tag observ The tag is the complete label The count is the number of occurrences in a training corpus E g NCMS000 33438 e Section lt LidstoneLambda gt specifies the A parameter for Lids
115. r gt lt equivocar gt lt inmutar gt lt morir gt lt ir gt lt manifestar gt lt precipitar gt lt referir gt lt venir gt VerbPron Animal 00008019 00863992 00865638 00868297 00990770 01813568 00862484 00864099 00867302 00868486 01420347 01883430 00862617 00864394 00867448 00868585 01586897 01947400 00862750 00865075 00867773 00868729 01661105 07400072 00862871 00865379 00867864 00911889 01661246 07501137 00863425 00865569 00868028 00985200 01664986 Constraint definition The CONSTRAINTS section consists of a series of context constraits each of the form weight core context Where e weight is a real value stating the compatibility or incompatibility if negative degree of the label with the context e core indicates the analysis or analyses form interpretation in a word that will be affected by the constraint It may be 40 CHAPTER 3 ANALYSIS MODULES Plain tag A plain complete PoS tag e g VMIP3S0 Wildcarded tag A PoS tag prefix right wilcarded e g VMI VMIPx Lemma A lemma enclosed in angle brackets optionaly preceded by a tag or a wild carded tag e g lt comer gt VMIP3S0 lt comer gt VMI lt comer gt will match any word analysis with those tag prefix and lemma Form Form enclosed in parenthesis preceded by a PoS tag or a wilcarded tag e g VMIP3S0 comi VMI
116. r gt will match any word analysis with those tag prefix and lemma x Form Form enclosed in parenthesis optionally preceded by a PoS tag or a wilcarded tag e g comi VMIP3S0 comi VMI comi will match any word analysis with those tag prefix and form Note that contrarily to when defining the rule core the form alone is allowed in the context Sense A sense code enclosed in square brackets optionaly preceded by a tag or a wildcarded tag e g 00862617 NCMSO00 00862617 NC 00862617 will match any word analysis with those tag prefix and sense x Set reference A name of a previously defined SET in curly brackets e g DetMasc VerbPron will match any word analysis with a tag lemma or sense in the speci fied set barrier states that the a match of the first term list is only acceptable if between the focus word and the matching word there is no match for the second term list Note that the use of sense information in the rules of the constraint grammar either in the core or in the context only makes sense when this information distinguishes one analysis from another If the sense tagging has been performed with the option DuplicateAnalysis no each PoS tag will have a list with all analysis so the sense information will not distinguish one analysis from the other there will be only one analysis with that sense which will have at the same time all the other senses as well If the option DuplicateAnalysis w
117. r languages that do not have an implementation of a specific automata a generic module is used to recognize number like expressions that contain nummerical digits There is no configuration file to be provided to the class when it is instantiated The API of the class is class numbers public Constructor receives the language code and the decimal and thousand point symbols numbers const std string amp const std string amp const std string amp 3 6 PUNCTUATION DETECTION MODULE 15 analyze given sentence void analyze sentence 4 analyze given sentences void analyze std list lt sentence gt 4 return analyzed copy of given sentence sentence analyze const sentence 4 return analyzed copy of given sentences std list lt sentence gt analyze const std list lt sentence gt amp The parameters that the constructor expects are e The language code used to decide whether the generic recognizer or a language specific module is used e The decimal point symbol e The thousand point sympol The last two parameters are needed because in some latin languages the comma is used as decimal point separator and the dot as thousand mark while in languages like English it is the other way round These parameters make it possible to specify what character is to be expected at each of these positions They will usually be comma and dot but any character could be used 3 6 Punctuation Detect
118. r launches each processor in a separate thread so while one sentece is being parsed the next is being tagged and the following one is running through the morphological analyzer In this way the multi core capabilities of the host are better exploited and the analyzer runs faster Although it is intended mainly as an example for developers wanting to build their own threaded applications this program can also be used to analyze texts in the same way than analyzer Nevertheless notice that this example program does not include modules that are not token or sentence oriented namely language identification and coreference resolution 5 3 Usage example Assuming we have the following input file mytext txt El gato come pescado Pero a Don Jaime no le gustan los gatos we could issue the command analyze f myconfig cfg lt mytext txt gt mytext mrf Assuming that myconfig cfg is the file presented in section 5 4 2 Given the options there the produced output would correspond to morfo format i e morphological analysis but no PoS tagging The expected results are El el DAOMSO 1 gato gato NCMSOOO 1 come comer VMIP3S0 0 75 comer VMM02S0 0 25 pescado pescado NCMS000 0 833333 pescar VMPOOSM 0 166667 Fp 1 Pero pero CC 0 99878 pero NCMS000 0 00121951 Pero NP00000 0 00121951 a a NCFSO00 0 0054008 a SPS00 0 994599 Don_Jaime Don_Jaime NPOOOOO 1 no no NCMS000 0 00231911 no RN 0 997681 le l PP3CSDOO 1 gustan gustar VMIP3PO 1 los
119. r the original form before affix removal e g crucecitas R stands for root found in dictionary after affix removal and root reconstruction e g cruces L stands for lemma in matching dictionary entry e g cruz A stands for the affix that the rule removed 9 Try the affix always not only for unknown words 10 Retokenization info explained below for none Example of prefix rule anti NC AQOCNO 0 00 1 ALO 20 CHAPTER 3 ANALYSIS MODULES This prefix rule states that anti should be removed from the beggining of the word nothing should be added and the resulting root should be found in the dictionary with a NC PoS tag If that is satisfied the word will receive the AQOCNO tag and its lemma will be set to the affix anti plus the lemma of the root found in the dictionary For instance the word antimisiles would match this rule misiles would be found in the dictionary with lema misil and PoS NCMPOOO Then the word will be assigned the lemma antimisil A L anti misil and the tag AQOCNO Examples of sufiz rules cecita zlza NCFS NCFSOOA O O 1 L 0 les V O 1 0O L 1 les PP The first suffix rule above cecita states a suffix rule that will be applied to unknown words to see whether a valid feminine singular noun is obtained when substituting the suffix cecita with z ot za This is the case of crucecita diminutive of cruz If such a base form is found the original word is analyzed as diminutive suf
120. ries if your dictionary uses another tagset unless you specify the right patterns to look for For instance if our dictionary uses Penn Treebank like tags we should define lt RE_NounAdj gt NN NNS JJ lt RE_NounAdj gt lt RE_Closed gt D IN C lt RE_Closed gt 3 11 NAMED ENTITY RECOGNITION MODULE 25 e Section lt TitleLimit gt contains only one line with an integer value stating the length beyond which a sentence written entirely in uppercase will be considered a title and not a proper noun Example lt TitleLimit gt 3 lt TitleLimit gt If TitleLimit 0 the default title detection is deactivated i e all uppercase sentences are always marked as named entities The idea of this heuristic is that newspaper titles are usually written in uppercase and tend to have at least two or three words while named entities written in this way tend to be acronyms e g IBM DARPA and usually have at most one or two words For instance if TitleLimit 3 the sentence FREELING ENTERS NASDAC UNDER CLOSE OB SERVATION OF MARKET ANALYSTS will not be recognized as a named entity and will have its words analyzed independently On the other hand the sentence IBM INC having less than 3 words will be considered a proper noun Obviously this heuristic is not 100 accurate but in some cases e g if you are analyzing newspapers it may be preferrable to the default behaviour which is not 100 accurate either
121. rogram that does exactly what I need In the directory src main simple_examples in the tarball you can find simpler sample pro grams that illustrate how to call the library and that can be used as a starting point to develop your own application 2 4 Porting to other platforms The FreeLing library is entirely written in C so it should be possible to compile it on non unix platforms with a reasonable effort Success have been reported on compiling FreeLing on MacOS as well as on MS Windows using cygwin http www cygwin com Also since version 3 0 project files are provided that should allow to compile FreeLing under MSVC Note that this files are contributor provided and not supported by FreeLing developers You ll find the files and a thorough README in the msvc folder inside FreeLing tarball You can visit the Forum amp FAQs sections in FreeLing webpage for further help and details Chapter 3 Analysis Modules This chapter describes each of the modules in FreeLing For each module its public API is described as well as its main configuration options Most modules can be customized via a configuration file A typical module receives a list of sentences and enriches them with new linguistic information morphological analysis disambiguation parsing etc Usually when the module is instantiated it receives as a parameter the name of a file where the information and or parameters needed by the module is stored e g a di
122. rol whether contractions are splitted by the dictionary module itself thus passing two words instead of one to later modules or the decision is left to later modules which will receive a single word carrying retokenization informa tion 3 9 2 Affixation Rules File The submodule of the dictionary handler that deals with affixes requires a set of affixation rules The file consists of two optional sections lt Suffixes gt and lt Prefixes gt The first one contains suffixation rules and the second prefixation rules They may appear in any order Both kinds of rules have the same format and only differ in whether the affix is checked at the beggining or at the end of the word Each rule has to be written in a different line and has 10 fields 1 Affix to erase form word form e g crucecita cecita cru 2 Affix for emtpy string to add to the resulting root to rebuild the lemma that must be searched in dictionary e g cru z cruz 3 Condition on the tag of found dictionary entry e g cruz is NCFS The condition is a perl RegExp Tag for suffixed word keep tag in dictionary entry Check lemma adding accents Enclitic suffix special accent behaviour in Spanish Prevent later modules e g probabilities from assigning additional tags to the word S SN OY NE ES Lemma to assign Any combination of F R L A or a string literal separated with a sign For instance R A A L R mente etc F stands fo
123. rtdict lt FLSHARE es senses30 src gt FLSHARE es senses30 ukb Obviously if you want to convert the file for a language different than Spanish you have change the path accordingly 3 17 Part of Speech Tagger Module There are two different modules able to perform PoS tagging The application should decide which method is to be used and instantiate the right class The first PoS tagger is the hmm_tagger class which is a classical trigam Markovian tagger following Bra00 The second module named relax_tagger is a hybrid system capable to integrate statistical and hand coded knowledge following Pad98 The hmm_tagger module is somewhat faster than relax_tagger but the later allows you to add manual constraints to the model Its API is the following class hmm_tagger public POS_tagger public Constructor hmm_tagger const std string amp const std string amp bool unsigned int unsigned int kb 1 analyze given sentence void analyze sentence amp analyze given sentences void analyze std list lt sentence gt amp return analyzed copy of given sentence sentence analyze const sentence amp return analyzed copy of given sentences std list lt sentence gt analyze const std list lt sentence gt amp 2 FLSHARE refers to the share FreeLing directory in your FreeLing installation which defaults to usr local share FreeLing if you installed from source usr share FreeLing if you u
124. ry for the Viterbi algorithm used to annotate NEs in a sentence lt InitialProb gt B 0 200072 I 0 0 0 0 799928 lt InitialProb gt Section lt TransitionProb gt Contains the transition probabilities for each class to each other class used by the Viterbi algorithm lt TransitionProb gt B B 0 00829346 B I 0 395481 B 0 0 596225 I B 0 0053865 I I 0 479818 I 0 0 514795 O B 0 0758838 0 I 0 0 0 0 0 924116 lt TransitionProb gt Section lt TitleLimit gt contains only one line with an integer value stating the length beyond which a sentence written entirely in uppercase will be considered a title and not a proper noun Example lt TitleLimit gt 3 lt TitleLimit gt If TitleLimit 0 the default title detection is deactivated i e all uppercase sentences are always marked as named entities The idea of this heuristic is that newspaper titles are usually written in uppercase and tend to have at least two or three words while named entities written in this way tend to be acronyms e g IBM DARPA and usually have at most one or two words 28 CHAPTER 3 ANALYSIS MODULES For instance if TitleLimit 3 the sentence FREELING ENTERS NASDAC UNDER CLOSE OB SERVATION OF MARKET ANALYSTS will not be recognized as a named entity and will have its words analyzed independently On the other hand the sentence IBM INC having less than 3 words will be considered a proper noun Obviously this heuristic is not 10
125. s texts or as examples of how to build customized applications using FreeLing 1 2 What is NOT FreeLing FreeLing is not a user oriented text analysis tool That is it is not designed to be user friendly or to output results with a cute image or in a certain format FreeLing results are linguistic analysis in a data structure Each end user application e g anything from a simple syntactic tree drawing plugin to a complete machine translation system can access those data and process them as needed Nevertheless FreeLing package provides a quite complete application program analyzer that enables an end user with no programming skills to obtain the analysis of a text See chapter 5 for details This program offers a set of options that cover most of FreeLing capabilities Nevertheless much more advantadge can be taken of FreeLing and more information can be accessed if you call FreeLing from your own application program 2 CHAPTER 1 INTRODUCTION 1 3 Supported Languages The current version supports to different extents see Table 1 3 Asturian as Catalan ca English en Galician gl Italian it Portuguese pt Russian ru Spanish es and Welsh cy Tokenization Sentence splitting Number detection Date detection Morphological dictionary Affix rules Multiword detection Basic named entity detection B I O named entity detection Named Entity Classification Quantity detection PoS tagging X Phonetic enc
126. sed a binary deb package You can change this environment variable to use different linguistic data files 36 CHAPTER 3 ANALYSIS MODULES given an analyzed sentence find out probability of the k th best sequence double SequenceProb_log const sentence amp int k 0 The hmm_tagger constructor receives the following parameters e The language code Used to determine if the language uses an EAGLES tagset and to properly shorten the PoS tags in that case e The HMM file which containts the model parameters The format of the file is described be low This file can be generated from a tagged corpus using the script src utilities train tagger bin TRAIN provided in FreeLing package See src utilities train tagger README to find out the details e A boolean stating whether words that carry retokenization information e g set by the dictionary or affix handling modules must be retokenized that is splitted in two or more words after the tagging e An integer stating whether and when the tagger must select only one analysis in case of ambiguity Possbile values are FORCE_NONE or 0 no selection forced words ambiguous after the tagger remain ambiguous FORCE_TAGGER or 1 force selection immediately after tagging and before retokenization FORCE_RETOK or 2 force selection after retok enization e An integer stating how many best tag sequences the tagger must try to compute If not specified this parameter defaults to 1
127. sible lemmas for the word pt List of all possible short PoS tags for the word pT List of all possible full PoS tags for the word na Number of analysis of the word e u i i th element of the word user field Note that all word properties including na are either strings or lists of strings The available primitive operations to build single conditions are the following 1 2 lt property gt is lt string gt String identity lt property gt matches lt regexp gt Regex match If the regex is parenthesized sub expression matches 0 1 2 etc are stored and can be used in the feature name pattern lt property list gt any_in_set lt filename gt or simply in_set True if any property in the list is found in the given file lt property list gt all_in_set lt filename gt True if all properties in the list are found in the given file 3 24 FEATURE EXTRACTION MODULE 59 5 lt property list gt some_in_set lt filename gt True if at least two properties in the list are found in the given file Operators can be negated with the character E g is matches etc For file operators expecting lists the property may be a single string list of one element Some sample valid conditions t is NC true if the short version of the tag equals NC T matches NC S true ifthe long version of the tag matches the given regular expression pl in _set my data files goodlemmas dat true if any possible lemma for
128. started studying Spanish two years ago is not a named entity If the words in the list appear with other capitalized words they are considered to form a named entity e g An announcement of the Spanish Bank of Commerce was issued yesterday The same distinction applies to the word Jin the sentences whatever you say I don t believe and That was the death of Henry I Each word or tag is followed by a 0 or 1 indicating whether the ignore condition is strict 0 non strict 1 strict The entries marked as non strict will have the behaviour described above The entries marked as strict will never be considered named entities or NE parts For instance the following lt Ignore gt section states that the word I is not to be a proper noun whatever you say I don t believe unless some of its neighbour words are That was the death of Henry I It also states that any word with the RB tag and any of the listed language names must never be considered as possible NEs 24 CHAPTER 3 ANALYSIS MODULES lt Ignore gt i 0 RB 1 english 1 dutch 1 spanish 1 lt Ignore gt Section lt Names gt contains a list of lemmas that may be names even if they conflict with some of the heuristic criteria used by the NE recognizer This is useful when they appear capitalized at sentence beggining For instance the basque name Miren Mary or the nickname Pel may appear at the beggining of a Spanish sentence Since both of them are verbal forms in
129. sts of one form per line each line with format form ambiguity class tagl observi tag2 observ2 E g japonesas AQ NC AQ 1 NC O Form probabilities are smoothed to avoid zero probabilities e Section lt ClassTagFreq gt Probability data of ambiguity classes If the word is not found in the lt FormTagFreq gt frequencies for its ambiguity class are used The list consists of class per line each line with format class tagl observ1 tag2 observ2 E g AQ NC AQ 2361 NC 2077 Class probabilities are smoothed to avoid zero probabilities e Section lt SingleTagFreq gt Unigram probabilities If the ambiguity class is not found in the lt ClassTagFreq gt individual frequencies for its possible tags are used One tag per line each line with format tag observ E g AQ 7462 Tag probabilities are smoothed to avoid zero probabilities e Section lt Theeta gt Value for parameter theeta used in smoothing of tag probabilities based on word suffixes If the word is not found in dictionary and so the list of its possible tags is unknown the distribution is computed using the data in the lt Theeta gt lt Suffixes gt and lt UnknownTags gt sections The section has exactly one line with one real number E g lt Theeta gt 0 00834 lt Theeta gt e Section lt BiassSuffixes gt Weighted interpolation factor between class probability and word suffixes The section has exactly one line with one real numbe
130. t 5 1 2 Client server mode If server and port options are specified a server will be launched which starts listening for incoming requests E g analyze f en cfg server port 50005 amp To launch the server the script follows the same steps described in previous section but with the options server port 50005 passed to the final call to the analyzer executable Once the server is launched clients can request analysis to the server with analyzer_client 50005 lt myinput gt myoutput analyzer_client localhost 50005 lt myinput gt myoutput or from a remote machine analyzer_client my server com 50005 lt myinput gt myoutput analyzer_client 192 168 10 11 50005 lt myinput gt myoutput The server will fork a new process to attend each new client so you can have many clients being served at the same time You can control the maximum amount of clients being attended simutaneously in order to prevent a flood in your server with the option workers You can control the size of the queue of pending clients with option queue Clients trying to connect when the queue is full will receive a connection error See section 5 4 1 for details on these options 5 2 USING A THREADED ANALYZER 69 5 2 Using a threaded analyzer If Libboost_thread is installed the installation process will build the program threaded_analyzer This program behaves like analyzer and has almost the same options The program threaded_analyze
131. te that the masculine singular participle is not excluded since it is the only allowed after an auxiliary haber 3 17 2 Relaxation Labelling Constraint Grammar File The syntax of the file is based on that of Constraint Grammars KVHA95 but simplified in many aspects and modified to include weighted constraints An initial file based on statistical constraints may be generated from a tagged corpus using the src utilities train tagger bin TRAIN sh script provided with FreeLing Later hand written constraints can be added to the file to improve the tagger behaviour The file consists of two sections SETS and CONSTRAINTS Set definition The SETS section consists of a list of set definitions each of the form Set name element2 elementN Where the Set name is any alphanumeric string starting with a capital letter and the elements are either forms lemmas plain PoS tags or senses Forms are enclosed in parenthesis e g comimos lemmas in angle brackets e g lt comer gt PoS tags are alphanumeric strings starting with a capital letter e g NCMSO00 and senses are enclosed in square brackets e g 00794578 The sets must be homogeneous That is all the elements of a set have to be of the same kind Examples of set definitions element1 DetMasc DAOMSO DAOMPO DDOMSO DDOMPO DIOMSO DIOMPO DP1MSP DP1MPP DP2MSP DP2MPP DTOMSO DTOMPO DEOMSO DEOMPO AQOMSO AQOMPO lt dar_cuenta gt lt atrever gt lt arrepenti
132. the directroy name of your choice Alternatively you can get any previous release with a command like svn checkout http devel cpl upc edu freeling svn versions freeling 3 0 alfal myfreeling just replace 3 0 alfal with the right version number You can see which versions are avail able by pointing your web browser to http devel cpl upc edu freeling svn versions 4 Prepare local repositories for compilation cd myfreeling aclocal libtoolize autoconf automake a 5 Build and install FreeLing configure make sudo make install If you keep the svn directories you will be able to update to newer versions at any moment cd myfreeling svn update configure make sudo make install Depending on what changed in the repository you may need to issue aclocal autoconf automake a after svn update You may also need to issue make distclean and repeat the process from configure onwards T Note that FreeLing 2 x versions require the appropiate libomlet and libfries versions which you can checkout from the SVN in the same way Check the Download section in FreeLing webpage to find out which Omlet amp Fries version is suitable for each 2 x FreeLing version 8 CHAPTER 2 GETTING IT TO WORK 2 2 4 Locale related problems when installing If you get an error about bad locale when you enter make install or when you try to execute the analyzer sample program you probably need to generate some locales in your system
133. the tagger must perform retokenization after the appropriate analysis has been selected for each word This is closely related to affix analysis and PoS taggers see sections 3 9 2 and 3 17 for details e Force the selection of one unique tag Command line Configuration file force lt string gt TaggerForceSelect none tagger retok Determine whether the tagger must be forced to probably randomly make a unique choice and when none Do not force the tagger allow ambiguous output tagger Force the tagger to choose before retokenization i e if retokenization intro duces any ambiguity it will be present in the final output retok Force the tagger to choose after retokenization no remaining ambiguity See 3 17 for more information e Chart Parser Grammar File Command line Configuration file G lt filename gt grammar lt filename gt GrammarFile lt filename gt This file contains a CFG grammar for the chart parser and some directives to control which chart edges are selected to build the final tree See section 3 20 1 for details e Dependency Parser Rule File Command line Configuration file T lt filename gt txala lt filename gt DepTxalaFile lt filename gt Rules to be used to perform dependency analysis See section 3 21 1 for details e Coreference Resolution Command line Configuration file coref nocoref CoreferenceResolution yes yl
134. the word is found in the given file 1 in _set my data files badlemmas dat true if selected lemma for the word is not found in the given file w matches Always true Will set the match variable 0 to the last three characters of the word so it can be used in the feature name pattern 3 24 5 Adding custom feature functions Custom feature functions can be defined and called from the rgf file enclosed in curly brackets e g quoted 0 Calls to custom feature functions in the rgf file must have one integer parameter indicating a word position relative to the target word Actual code computing custom feature functions must be provided by the caller A map std map lt std wstring const feature_function gt needs to be given to the constructor as sociating the custom function as used in the rule file with a feature function pointer Custom feature functions must be classes derived from class feature function class feature_function public virtual void extract const sentence amp int std list lt std wstring gt amp const 0 Destructor virtual feature_function H They must implement a method extract that receives the sentence the position of the target word and a list of strings where the resulting feature name or names if more than one is to be generated will be added For instance the example below generates the feature name in quotes when the target word is surrounded by words with the Fe P
135. to note that the lemmatized multiword expressions the ones that containt angle brackets will only be recognized if the lemma is present in the dictionary with its corresponding inflected forms 30 CHAPTER 3 ANALYSIS MODULES 3 13 Probability Assignment and Unkown Word Guesser Module This class ends the morphological analysis subchain and has two functions first it assigns an a priori probability to each analysis of each word These probablities will be needed for the PoS tagger later Second if a word has no analysis none of the previously applied modules succeeded to analyze it this module tries to guess which are its possible PoS tags based on the word ending class probabilities public Constructor receives the language code the name of the file containing probabilities and a threshold probabilities const std string amp const std string amp double Assign probabilities for each analysis of given word void annotate_word word amp Turn guesser on of void set_activate_guesser bool analyze given sentence void analyze sentence amp analyze given sentences void analyze std list lt sentence gt amp return analyzed copy of given sentence sentence analyze const sentence amp return analyzed copy of given sentences std list lt sentence gt analyze const std list lt sentence gt amp The constructor receives e The language code It is used only to dec
136. tone s Law smoothing The section has exactly one line with one real number E g lt LidstoneLambda gt 0 2 lt LidstoneLambda gt Default value is 0 1 This parameter is used only to smooth the lexical probabilities of words that have appeared in the training corpus and thus are listed in the lt FormTagFreq gt section described above 3 14 Ortographic Correction Module This module takes into account phonetic similarities and tries to provide analysis for words that would be otherwise treated as unknown words For instance if a mispelled word such as cavallo is found this module will find out that it sounds the same than a correct word in the dictionary caballo thus providing analysis for that otherwise unknown word This module is under development and it is not operational yet 3 15 Sense Labelling Module This module searches the lemma of each analysis in a sense dictionary and enriches the analysis with the list of senses found there Note that this is not disambiguation all senses for the lemma are returned The module receives a file containing several configuration options which specify the sense dictionary to be used and some mapping rules that can be used to adapt FreeLing PoS tags to those used in the sense dictionary FreeLing provides WordNet based Fel98 Vos98 dictionaries but the results of this module can be changed to any other sense catalogue simply providing a different sense dictionary file cl
137. two lines with the format lt DataFiles gt relationFile common xwn30 ukb bin UKBSenseDictFile senses30 ukb lt DataFiles gt 3 17 PART OF SPEECH TAGGER MODULE 35 If the mapping rules lt WNposMap gt require a form dictionary a keyword formDictFile with the dictionary to use must be provided in this section More details are given in section 3 23 relationFile specifies the file name of the semantic relationship graph to load This is a binary file containing pairs of related senses WN synsets in this case Relations are not labelled nor directed This file is created by FreeLing installation scripts from the plain files extracted from UKB package data common wnet30 rels txt and data common wnet30g rels txt You can re create it or create a new one using a different relation set with the following commands cd FLSHARE common compile_ukb o wn30 ukb bin wnet30 rels txt compile_ukb o xwn30 ukb bin wnet30 rels txt wnet30g rels txt The parameter UKBSenseDictFile is a sense dictionary with the possible senses for each word Since this is the same information contained in the sense file for the Senses module described in section 3 15 this file is generated by FreeLing installation scripts simply converting the default sense file see sections 3 15 and 3 23 2 to the appropriate format You can re create this file or use any other sense dictionary with the right format with the command paths may differ conve
138. ty to a 3rd person personal pronoun being of undefined gender and number PP3CNAOO if it has the possibility of being masculine singular PP3MSA00 the next word may have lemma estar to be and the second word to the right is not a gerund VMG This rule is intended to solve the different behaviour of the Spanish word lo it in sentences such as Cansado Si lo estoy Tired Yes I am it or lo estoy viendo I am watching it 0 5 PP3CNAOO O PP3MSA00 1 lt estar gt not 2 VMGx 3 18 Phonetic Encoding Module The phonetic encoding module enriches words with their SAMPA phonetic codification The module applies a set of rules to transform the written form to the output phonetic form thus the rules can be changed to get the output in any other phonetic alphabet The module can also use an exception dictionary to handle forms that do not follow the default rules or for languages with highly irregular ortography The API of the module is the following class phonetics public Constructor given config file phonetics const std wstring amp Returns the phonetic sound of the word std wstring get_sound const std wstring amp analyze given sentence enriching words with phonetic encoding void analyze sentence amp analyze given sentences void analyze std list lt sentence gt amp analyze sentence return analyzed copy sentence analyze const sentence amp Swww pho
139. uage analysis services such as morpho logical analysis date recognition PoS tagging etc The current version provides language identification tokenizing sentence splitting morpho logical analysis NE detection and classification recognition of dates numbers physical magni tudes currency ratios phonetic encoding PoS tagging shallow parsing dependency parsing WN based sense annotation Word Sense Disambiguation and coreference resolution Future versions are expected to improve performance in existing functionalities as well as incorporate new features FreeLing is designed to be used as an external library from any application requiring this kind of services Nevertheless a sample main program is also provided as a basic interface to the library which enables the user to analyze text files from the command line 1 1 What is FreeLing FreeLing is a developer oriented library providing language analysis services If you want to develop say a machine translation system and you need some kind of linguistic processing of the source text your MT application can call FreeLing modules to do the required analysis In the directory src main simple_examples in FreeLing tarball some sample programs are provided to illustrate how an application program can call the library In the directory src main sample_analyzer a couple of more complex programs are provided which can be used either as a command line interface to the library to proces
140. unker with a head word with form cats np lt cat gt any chunk labeled np by the chunker with a head word with lemma cat np animal any chunk labeled np by the chunker with a head word with a lemma in animal category see CLASS section below np N M any chunk labeled np by the chunker with a head word with a PoS tag matching the N N regular expression e operation is the way in which ancestor and descendant nodes are to be combined see below e The op params component has two meanings depending on the operation field top_left and top_right operations must be followed by the literal RELABEL plus the new label s to assign to the chunks Other operations must be followed by the literal MATCHING plus the label to be matched For top_left and top_right operations the labels following the keyword RELABEL state the labels with which each chunk in the pair will be relabelled in the format label1 label2 If specified label1 will be the new label for the left chunk and label2 the one for the right chunk A dash means no relabelling In none of both chunks is to be relabelled may be used instead of For example the rule 20 np pp lt of gt top_left RELABEL np of will hang the pp chunk as a daugh ter of the left chunk in the pair i e np then relabel the np to np of and leave the label for the pp unchanged For last_left last_right and cover_last_left operati
141. v libicu dev libboost filesystem dev libboost program options dev OpenSuse Fedora Mandriva boost devel boost regex devel libicu devel boost filesystem devel boost program options devel Slackware boost icu4c Note that you need to install both the binary libraries and the development packages usually sufixed as dev or devel Most package managers will install both binary and development packages when the dev package is required If this is not your case you ll need to manually install both packages See details on the installation procedure in section 2 2 2 2 Installation This section provides a detailed guide on different options to install FreeLing and all its required packages 6 CHAPTER 2 GETTING IT TO WORK 2 2 1 Install from deb binary packages This installation procedure is the fastest and easiest If you do not plan to modify the code this is the option you should take Binary packages are available only for stable FreeLing versions If you want to install an alpha or beta version please see sections 2 2 2 and 2 2 3 The provided packages will only work on debian based distributions They have been tested in Ubuntu 10 04LTS Lucid 11 04 Natty 10 11 Oneiric 12 04 Precise and Debian 6 0 2 Squeeze 7 0 Wheezy The installation procedure for these distributions will probably work for other debian based distros is the following sudo apt get install libboost regex dev libicu dev sudo apt get install li
142. well to estimate the likely tags for unknown words See section 3 13 for details 5 4 CONFIGURATION FILE AND COMMAND LINE OPTIONS TT e Unknown Words Probability Threshold Command line Configuration file e lt float gt thres lt float gt ProbabilityThreshold lt float gt Threshold that must be reached by the probability of a tag given the suffix of an unknown word in order to be included in the list of possible tags for that word Default is zero all tags are included in the list A non zero value e g 0 0001 0 001 is recommended e Named Entity Recognition Command line Configuration file ner biolbasic none NERecognition bio basic none Whether to perform NE recognition and which recognizer to use bio for AdaBoost based NER basic for a simple heuristic NE recognizer and none to perform no NE recognition Deactivating this feature will cause the NE Classification module to have no effect e Named Entity Recognition Command line Configuration file ner noner NERecognition yes y on no n off Whether to perform NE recognition e Named Entity Recognizer File Command line Configuration file N lt filename gt fnp lt filename gt NPDataFile lt filename gt Configuration data file for NE recognizer See section 3 11 for details e Named Entity Classification Command line Configuration
143. what is dealt with through affixation rules is left to the linguist building the linguistic data resources The API for this module is the following class dictionary public Constructor dictionary const std string amp const std string amp bool const std string amp bool bool Get analysis for a given form and add them 18 CHAPTER 3 ANALYSIS MODULES to given analysis list void search_form const std string amp std list lt analysis gt amp Get possible forms for a lemmatpos std list lt std wstring gt get_forms const std wstring amp const std wstring amp const analyze given sentence void analyze sentence 4 analyze given sentences void analyze std list lt sentence gt amp return analyzed copy of given sentence sentence analyze const sentence 4 return analyzed copy of given sentences std list lt sentence gt analyze const std list lt sentence gt amp The parameters of the constructor are e The language of the processed text This is required by the affixation submodule to properly handle graphical accent rules in latin languages e The dictionary file name See below for details e A boolean stating whether affixation analysis has to be applied e The affixation rules file name it may be an empty string if the boolean above is set to false e An optional boolean default false stating whether the dictionary must be created with inverse ac

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