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Verb Valency Frames Disambiquation
dc.creatorSemecký, Jiří
dc.date.accessioned2021-05-19T16:40:49Z
dc.date.available2021-05-19T16:40:49Z
dc.date.issued2008
dc.identifier.urihttp://hdl.handle.net/20.500.11956/12165
dc.description.abstractSemantic analysis has become a bottleneck of many natural language applications. Machine translation, automatic question answering, dialog management, and others rely on high quality semantic analysis. Verbs are central elements of clauses with strong influence on the realization of whole sentences. Therefore the semantic analysis of verbs plays a key role in the analysis of natural language. We believe that solid disambiguation of verb senses can boost the performance of many real-life applications. In this thesis, we investigate the potential of statistical disambiguation of verb senses. Each verb occurrence can be described by diverse types of information. We investigate which information is worth considering when determining the sense of verbs. Different types of classification methods are tested with regard to the topic. In particular, we compared the Na¨ive Bayes classifier, decision trees, rule-based method, maximum entropy, and support vector machines. The proposed methods are thoroughly evaluated on two different Czech corpora, VALEVAL and the Prague Dependency Treebank. Significant improvement over the baseline is observed.en_US
dc.languageEnglishcs_CZ
dc.language.isoen_US
dc.publisherUniverzita Karlova, Matematicko-fyzikální fakultacs_CZ
dc.titleVerb Valency Frames Disambiquationen_US
dc.typerigorózní prácecs_CZ
dcterms.created2008
dcterms.dateAccepted2008-01-14
dc.description.departmentInstitute of Formal and Applied Linguisticsen_US
dc.description.departmentÚstav formální a aplikované lingvistikycs_CZ
dc.description.facultyFaculty of Mathematics and Physicsen_US
dc.description.facultyMatematicko-fyzikální fakultacs_CZ
dc.identifier.repId50522
dc.title.translatedVerb Valency Frames Disambiquationcs_CZ
dc.identifier.aleph001138311
thesis.degree.nameRNDr.
thesis.degree.levelrigorózní řízenícs_CZ
thesis.degree.disciplineData Engineeringen_US
thesis.degree.disciplineDatové inženýrstvícs_CZ
thesis.degree.programInformaticsen_US
thesis.degree.programInformatikacs_CZ
uk.thesis.typerigorózní prácecs_CZ
uk.taxonomy.organization-csMatematicko-fyzikální fakulta::Ústav formální a aplikované lingvistikycs_CZ
uk.taxonomy.organization-enFaculty of Mathematics and Physics::Institute of Formal and Applied Linguisticsen_US
uk.faculty-name.csMatematicko-fyzikální fakultacs_CZ
uk.faculty-name.enFaculty of Mathematics and Physicsen_US
uk.faculty-abbr.csMFFcs_CZ
uk.degree-discipline.csDatové inženýrstvícs_CZ
uk.degree-discipline.enData Engineeringen_US
uk.degree-program.csInformatikacs_CZ
uk.degree-program.enInformaticsen_US
thesis.grade.csUznánocs_CZ
thesis.grade.enRecognizeden_US
uk.abstract.enSemantic analysis has become a bottleneck of many natural language applications. Machine translation, automatic question answering, dialog management, and others rely on high quality semantic analysis. Verbs are central elements of clauses with strong influence on the realization of whole sentences. Therefore the semantic analysis of verbs plays a key role in the analysis of natural language. We believe that solid disambiguation of verb senses can boost the performance of many real-life applications. In this thesis, we investigate the potential of statistical disambiguation of verb senses. Each verb occurrence can be described by diverse types of information. We investigate which information is worth considering when determining the sense of verbs. Different types of classification methods are tested with regard to the topic. In particular, we compared the Na¨ive Bayes classifier, decision trees, rule-based method, maximum entropy, and support vector machines. The proposed methods are thoroughly evaluated on two different Czech corpora, VALEVAL and the Prague Dependency Treebank. Significant improvement over the baseline is observed.en_US
uk.file-availabilityV
uk.grantorUniverzita Karlova, Matematicko-fyzikální fakulta, Ústav formální a aplikované lingvistikycs_CZ
thesis.grade.codeU
uk.publication-placePrahacs_CZ
uk.thesis.defenceStatusU
dc.identifier.lisID990011383110106986


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