Boosting Applied to Word Sense Disambiguation

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dc.creatorEscudero, Gerard
dc.creatorMarquez, Lluis
dc.creatorRigau, German
dc.date2000-07-07
dc.date.accessioned2026-07-25T23:56:06Z
dc.descriptionIn this paper Schapire and Singer's AdaBoost.MH boosting algorithm is applied to the Word Sense Disambiguation (WSD) problem. Initial experiments on a set of 15 selected polysemous words show that the boosting approach surpasses Naive Bayes and Exemplar-based approaches, which represent state-of-the-art accuracy on supervised WSD. In order to make boosting practical for a real learning domain of thousands of words, several ways of accelerating the algorithm by reducing the feature space are studied. The best variant, which we call LazyBoosting, is tested on the largest sense-tagged corpus available containing 192,800 examples of the 191 most frequent and ambiguous English words. Again, boosting compares favourably to the other benchmark algorithms.
dc.description12 pages
dc.identifierhttps://arxiv.org/abs/cs/0007010
dc.identifierhttp://arxiv.org/abs/cs/0007010
dc.identifierProceedings of the 11th European Conference on Machine Learning, ECML'2000 pp. 129-141
dc.identifier.urihttps://dspace.dare.co.zw/handle/123456789/102629
dc.subjectComputation and Language
dc.subjectArtificial Intelligence
dc.subjectI.2.7;I.2.6
dc.titleBoosting Applied to Word Sense Disambiguation
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