Tesis doctoral en Informática realizada por Rubén Izquierdo en la Universidad de Alicante (UA) bajo la dirección del Dr. Armando Suárez Cueto (UA) y del Dr. German Rigau Claramunt (EHU/UPV). El acto de defensa de la tesis tuvo lugar en Alicante el 17 de Septiembre de 2010 ante el tribunal formado por los doctores Manuel Palomar (UA), Paloma Moreda (UA), María Teresa Martín (UJA), Lluís Padró (UPC) e Irene Castellón (UB). La calificación obtenida fue Sobresaliente Cum Laude por unanimidad.Ph.D Thesis in Computer Science, specifically in the field of Computational Linguistics, written by Rubén Izquierdo at the University of Alicante (UA), under the supervision of Dr. Armando Suárez Cueto (UA) and Dr. German Rigau Claramunt (EHU/UPV). The author was examined on September 17th 2010, by a panel formed by Dr. Manuel Palomar (UA), Dr. Paloma Moreda (UA), Dr. María Teresa Martín (UJA), Dr. Lluís Padró (UPC) and Dr. Irene Castellón (UB). The grade obtained was Sobresaliente Cum Laude.Este trabajo ha sido co-financiado por el Ministerio de Ciencia e Innovación (proyecto TIN2009-13391-C04-01), y la Conselleria de Educación de la Generalitat Valenciana (proyectos PROMETEO/2009/119 y ACOMP/2011/001)

Izquierdo Beviá, Rubén

Repositorio Institucional de la Universidad de Alicante

An Approach to Word Sense Disambiguation based on SemanticClasses and Machine Learning∗Una Aproximacio´n a la Desambiguacio´n del Sentido de las Palabrasbasada en Clases Sema´nticas y Aprendizaje Automa´ticoRube´n IzquierdoDepartamento Lenguajes y Sistemas Informa´ticosUniversidad de AlicanteAptdo. Correos 99 E-03080 Alicanteruben@dlsi.ua.esResumen: Tesis doctoral en Informa´tica realizada por Rube´n Izquierdo en la Uni-versidad de Alicante (UA) bajo la direccio´n del Dr. Armando Sua´rez Cueto (UA)y del Dr. German Rigau Claramunt (EHU/UPV). El acto de defensa de la tesistuvo lugar en Alicante el 17 de Septiembre de 2010 ante el tribunal formado por losdoctores Manuel Palomar (UA), Paloma Moreda (UA), Mar´ıa Teresa Mart´ın (UJA),Llu´ıs Padro´ (UPC) e Irene Castello´n (UB). La calificacio´n obtenida fue SobresalienteCum Laude por unanimidad.Palabras clave:Word Sense Disambiguation, Semantic Classes, Machine Learning,Semantic ClassifiersAbstract: Ph.D Thesis in Computer Science, specifically in the field of Compu-tational Linguistics, written by Rube´n Izquierdo at the University of Alicante (UA),under the supervision of Dr. Armando Sua´rez Cueto (UA) and Dr. German RigauClaramunt (EHU/UPV). The author was examined on September 17th 2010, by apanel formed by Dr. Manuel Palomar (UA), Dr. Paloma Moreda (UA), Dr. Mar´ıaTeresa Mart´ın (UJA), Dr. Llu´ıs Padro´ (UPC) and Dr. Irene Castello´n (UB). Thegrade obtained was Sobresaliente Cum Laude.Keywords: Desambiguacio´n del Sentido de las Palabras, Clases Sema´nticas, Apren-dizaje Automa´tico, Clasificadores Sema´nticos1. IntroductionNatural language is extremely complex andambiguous. One of the main ambiguities thathas received more attention is the lexical am-biguity.Words can have more than one mea-ning, and therefore texts containing thesepolysemous words are ambiguous. Word Sen-se Disambiguation (WSD) is an enabling taskin Natural Language Processing (NLP) thattries to solve lexical ambiguity, or in otherwords, determine which is the proper senseof a polysemous word depending on its su-rrounding context.From a Machine Learning perspective,WSD can be seen as a classification pro-blem. From this point-of-view, a machine∗ Este trabajo ha sido co–financiado por el Ministe-rio de Ciencia e Innovacio´n (proyecto TIN2009-13391-C04-01), y la Conselleria de Educacio´n de la Genera-litat Valenciana (proyectos PROMETEO/2009/119 yACOMP/2011/001).learning algorithm learns classification mo-dels from corpora annotated with word sen-ses. The most used repository of senses hasbeen WordNet. This kind of supervised ap-proaches based on word senses have shownto reach a very high performance on Sen-sEval/SemEval competitions. However, sin-ce SensEval–3 there is a general consensusabout that traditional word sense–based ap-proaches has reached a plateau very difficultto overcome. One of the possible reasons isthat WordNet, as a repository of senses, pro-vides too fine–grained senses, with very littledifferences between senses of the same word.This kind of differences are too subtle to becaptured by a machine learning system. Thisproblem is even more acute when the amountof training examples for each classifier is lowfor ensuring a robust generalization. Further-more, the existing sense annotated resourcesare not very large. This phenomenon is usua-Procesamiento del Lenguaje Natural, Revista nº 46 marzo de 2011, pp 123-124 recibido 27-09-10 revisado 22-02-11 aceptado 24-02-11ISSN 1135-5948 © 2011 Sociedad Española para el Procesamiento del Lenguaje Naturallly known as the knowledge acquisition bottle-neck.This research focuses on the use of seman-tic classes instead of traditional word senses.A semantic class is a concept that groups aset of sub–concepts that present lexical cohe-rence and share certain properties. Examplesof semantic classes are building, vehicleor food. The use of semantic classes provi-des several advantages compared with the useof senses: reduction of polysemy, higher le-vel of abstraction for WSD, increment of theamount of training data available for eachclassifier and also the possibility to disam-biguate words not contained in training da-ta. We have developed a semantic class basedsystem for WSD, using Support Vector Ma-chines (SVM) as a learning method, SemCoras a training corpus and two different setsof features. We also tests different sets of se-mantic classes: Basic Level Concepts (BLC),WordNet Domains, SUMO classes and Su-perSenses. Semantic classes has been used forbuilding both the classifiers and two types ofsemantic features.2. Contributions of this thesisThe main contributions of this thesis are two:an automatic method to extract BLC fromWordNet, and a semantic class based WSDsystem. Both the resources and the NLP toolare available for research1.2.1. Automatic extraction of BLCfrom WordNetWe have developed a method that automa-tically extracts a set of Basic Level Con-cepts from WordNet. The process follows abottom–up approach through the hypernymychains of WordNet. For each synset2, the al-gorithm selects as its BLC the first local ma-ximum (according to a certain criteria) in thehypernymy chain. To compute this local ma-ximum different characteristics can be consi-dered. For instance, the number of relationsof each synset or the frequency of the syn-set (computed as the sum of frequencies ofthe words within the synset). This criteria isa parameter of the algorithm. Another pa-rameter is the minimum number of conceptsthat each candidate must subsume to be anactual BLC. This is the second parameter ofthe method.1http://www.dlsi.ua.es/~ruben2The method is only defined for nouns and verbs.Combining different values for the two pa-rameters, different sets of BLC can be au-tomatically extracted from WordNet. Thisis very relevant since the method is flexibleenough to derive different sets of BLC at adifferent abstraction level. Possibly, a parti-cular NLP application requires a particularlevel of abstraction for representing its se-mantic information. Predefined set of classes(like WordNet Domains, SUMO or SuperSen-ses) have an static level of abstraction. Ourapproach provides a good alternative to thesepredefined semantic classes.2.2. A semantic class based WSDsystemOur WSD system uses a machine learning al-gorithm (SVM) and SemCor as training da-ta, two sets of features, and several semanticclass repositories: BLC, WordNet Domains,SUMO and SuperSenses. Each of the classi-fiers assigns the proper semantic class to eachambiguous word. We also use the semanticclasses to build two types of semantic featu-res to help the characterization of trainingand testing examples.We have tested the WSD system usingthe evaluation corpora from SensEval–2, SenseEval–3 and SemEval–1. Moreover,we have participated in SemEval–1 andSemEval–2 with our semantic class based sys-tem reaching the 5th best position in both ca-ses. Finally, we compare our system with theparticipants at SensEval–2 and SensEval–3 ata sense and a semantic class levels. In bothcases, the system outputs are transformed towork at the same abstraction level (sense orsemantic class) for a fair evaluation.This research empirically demonstratesthat we can select a medium level of abs-traction with semantic classes and reach ahigh performance (near to very abstract se-mantic classes) maintaining the discrimina-tory power of meanings. Moreover, the se-mantic class classifiers are more robust andmore independent to domain shifting thansense classifiers. Finally we have shown thatthe amount of training data available for eachclassifier is increased when using the semanticclass approach, alleviating slightly the lack ofsemantically annotated corpora.Rubén Izquierdo124

Una aproximación a la desambiguación del sentido de las palabras basada en clases semánticas y aprendizaje automático

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Una aproximación a la desambiguación del sentido de las palabras basada en clases semánticas y aprendizaje automático

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