Sequential Model Selection for Word Sense Disambiguation

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dc.creatorPedersen, Ted
dc.creatorBruce, Rebecca
dc.creatorWiebe, Janyce
dc.date1997-02-12
dc.date.accessioned2026-07-07T09:10:43Z
dc.date.available2026-07-07T09:10:43Z
dc.descriptionStatistical models of word-sense disambiguation are often based on a small number of contextual features or on a model that is assumed to characterize the interactions among a set of features. Model selection is presented as an alternative to these approaches, where a sequential search of possible models is conducted in order to find the model that best characterizes the interactions among features. This paper expands existing model selection methodology and presents the first comparative study of model selection search strategies and evaluation criteria when applied to the problem of building probabilistic classifiers for word-sense disambiguation.
dc.description8 pages, Latex, uses aclap.sty
dc.identifierhttps://arxiv.org/abs/cmp-lg/9702008
dc.identifierhttp://arxiv.org/abs/cmp-lg/9702008
dc.identifierProceedings of the Fifth Conference on Applied Natural Language Processing, April 1997, Washington, DC
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/151430
dc.subjectComputation and Language
dc.titleSequential Model Selection for Word Sense Disambiguation
dc.typetext

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