Comparative Experiments on Disambiguating Word Senses: An Illustration of the Role of Bias in Machine Learning

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dc.creatorMooney, Raymond J.
dc.date1996-12-09
dc.date.accessioned2026-07-07T09:10:41Z
dc.date.available2026-07-07T09:10:41Z
dc.descriptionThis paper describes an experimental comparison of seven different learning algorithms on the problem of learning to disambiguate the meaning of a word from context. The algorithms tested include statistical, neural-network, decision-tree, rule-based, and case-based classification techniques. The specific problem tested involves disambiguating six senses of the word ``line'' using the words in the current and proceeding sentence as context. The statistical and neural-network methods perform the best on this particular problem and we discuss a potential reason for this observed difference. We also discuss the role of bias in machine learning and its importance in explaining performance differences observed on specific problems.
dc.description10 pages
dc.identifierhttps://arxiv.org/abs/cmp-lg/9612001
dc.identifierhttp://arxiv.org/abs/cmp-lg/9612001
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/151419
dc.subjectComputation and Language
dc.titleComparative Experiments on Disambiguating Word Senses: An Illustration of the Role of Bias in Machine Learning
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