Naive Bayes and Exemplar-Based approaches to Word Sense Disambiguation Revisited

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dc.creatorEscudero, Gerard
dc.creatorMarquez, Lluis
dc.creatorRigau, German
dc.date2000-07-07
dc.date.accessioned2026-07-07T03:16:21Z
dc.date.available2026-07-07T03:16:21Z
dc.descriptionThis paper describes an experimental comparison between two standard supervised learning methods, namely Naive Bayes and Exemplar-based classification, on the Word Sense Disambiguation (WSD) problem. The aim of the work is twofold. Firstly, it attempts to contribute to clarify some confusing information about the comparison between both methods appearing in the related literature. In doing so, several directions have been explored, including: testing several modifications of the basic learning algorithms and varying the feature space. Secondly, an improvement of both algorithms is proposed, in order to deal with large attribute sets. This modification, which basically consists in using only the positive information appearing in the examples, allows to improve greatly the efficiency of the methods, with no loss in accuracy. The experiments have been performed on the largest sense-tagged corpus available containing the most frequent and ambiguous English words. Results show that the Exemplar-based approach to WSD is generally superior to the Bayesian approach, especially when a specific metric for dealing with symbolic attributes is used.
dc.description5 pages
dc.identifierhttps://arxiv.org/abs/cs/0007011
dc.identifierhttp://arxiv.org/abs/cs/0007011
dc.identifierProceedings of the 14th European Conference on Artificial Intelligence, ECAI'2000 pp. 421-425
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/30319
dc.subjectComputation and Language
dc.subjectArtificial Intelligence
dc.subjectI.2.7;I.2.6
dc.titleNaive Bayes and Exemplar-Based approaches to Word Sense Disambiguation Revisited
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