A Simple Approach to Building Ensembles of Naive Bayesian Classifiers for Word Sense Disambiguation

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dc.creatorPedersen, Ted
dc.date2000-05-07
dc.date.accessioned2026-07-07T03:16:11Z
dc.date.available2026-07-07T03:16:11Z
dc.descriptionThis paper presents a corpus-based approach to word sense disambiguation that builds an ensemble of Naive Bayesian classifiers, each of which is based on lexical features that represent co--occurring words in varying sized windows of context. Despite the simplicity of this approach, empirical results disambiguating the widely studied nouns line and interest show that such an ensemble achieves accuracy rivaling the best previously published results.
dc.description7 pages, Latex, uses colnaacl.sty. Appears in Proceedings of NAACL, pages 63-69, May 2000, Seattle, WA
dc.identifierhttps://arxiv.org/abs/cs/0005006
dc.identifierhttp://arxiv.org/abs/cs/0005006
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/30258
dc.subjectComputation and Language
dc.subjectI.2.7
dc.titleA Simple Approach to Building Ensembles of Naive Bayesian Classifiers for Word Sense Disambiguation
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