Unsupervised WSD by finding the predominant sense using context as a dynamic thesaurus

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dc.contributor.authorTejada Cálrcamo, Javier
dc.contributor.authorCalvo, Hiram
dc.contributor.authorGelbukh, Alex
dc.contributor.authorHara, Kazuo
dc.date.accessioned2019-01-29T22:19:56Z
dc.date.available2019-01-29T22:19:56Z
dc.date.issued2010
dc.description.abstractWe present and analyze an unsupervised method for Word Sense Disambiguation (WSD). Our work is based on the method presented by McCarthy et al. in 2004 for finding the predominant sense of each word in the entire corpus. Their maximization algorithm allows weighted terms (similar words) from a distributional thesaurus to accumulate a score for each ambiguous word sense, i.e., the sense with the highest score is chosen based on votes from a weighted list of terms related to the ambiguous word. This list is obtained using the distributional similarity method proposed by Lin Dekang to obtain a thesaurus. In the method of McCarthy et al., every occurrence of the ambiguous word uses the same thesaurus, regardless of the context where the ambiguous word occurs. Our method accounts for the context of a word when determining the sense of an ambiguous word by building the list of distributed similar words based on the syntactic context of the ambiguous word. We obtain a top precision of 77.54% of accuracy versus 67.10% of the original method tested on SemCor. We also analyze the effect of the number of weighted terms in the tasks of finding the Most Frecuent Sense (MFS) and WSD, and experiment with several corpora for building the Word Space Model. © 2010 Springer Science+Business Media, LLC & Science Press, China.es_PE
dc.description.uriTrabajo académicoes_PE
dc.identifier.doihttps://doi.org/10.1007/s11390-010-9385-2es_PE
dc.identifier.issn10009000es_PE
dc.identifier.urihttps://hdl.handle.net/20.500.12590/15898
dc.language.isoenges_PE
dc.publisherScopuses_PE
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-78650204798&doi=10.1007%2fs11390-010-9385-2&partnerID=40&md5=99192aac46b7a3081deac681de4bf27bes_PE
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_PE
dc.sourceRepositorio Institucional - UCSPes_PE
dc.sourceUniversidad Católica San Pabloes_PE
dc.sourceScopuses_PE
dc.subjectDistributional similaritieses_PE
dc.subjectMaximization algorithmes_PE
dc.subjectSemantic similarityes_PE
dc.subjectText corporaes_PE
dc.subjectUnsupervised methodes_PE
dc.subjectWord sensees_PE
dc.subjectWord Sense Disambiguationes_PE
dc.subjectWord spaceses_PE
dc.subjectSemanticses_PE
dc.subjectSoftware agentses_PE
dc.subjectThesauries_PE
dc.subjectNatural language processing systemses_PE
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#1.02.00es_PE
dc.titleUnsupervised WSD by finding the predominant sense using context as a dynamic thesauruses_PE
dc.typeinfo:eu-repo/semantics/article
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