A Comparison between Supervised Learning Algorithms for Word Sense Disambiguation

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dc.creatorEscudero, Gerard
dc.creatorMarquez, Lluis
dc.creatorRigau, German
dc.date2000-09-22
dc.date.accessioned2026-07-07T03:16:34Z
dc.date.available2026-07-07T03:16:34Z
dc.descriptionThis paper describes a set of comparative experiments, including cross-corpus evaluation, between five alternative algorithms for supervised Word Sense Disambiguation (WSD), namely Naive Bayes, Exemplar-based learning, SNoW, Decision Lists, and Boosting. Two main conclusions can be drawn: 1) The LazyBoosting algorithm outperforms the other four state-of-the-art algorithms in terms of accuracy and ability to tune to new domains; 2) The domain dependence of WSD systems seems very strong and suggests that some kind of adaptation or tuning is required for cross-corpus application.
dc.description6 pages
dc.identifierhttps://arxiv.org/abs/cs/0009022
dc.identifierhttp://arxiv.org/abs/cs/0009022
dc.identifierProceedings of the 4th Conference on Computational Natural Language Learning, CoNLL'2000, pp. 31-36
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/30402
dc.subjectComputation and Language
dc.subjectArtificial Intelligence
dc.subjectI.2.7;I.2.6
dc.titleA Comparison between Supervised Learning Algorithms for Word Sense Disambiguation
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