Exploring automatic word sense disambiguation with decision lists and the Web

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dc.creatorAgirre, Eneko
dc.creatorMartinez, David
dc.date2000-10-17
dc.date.accessioned2026-07-07T03:16:38Z
dc.date.available2026-07-07T03:16:38Z
dc.descriptionThe most effective paradigm for word sense disambiguation, supervised learning, seems to be stuck because of the knowledge acquisition bottleneck. In this paper we take an in-depth study of the performance of decision lists on two publicly available corpora and an additional corpus automatically acquired from the Web, using the fine-grained highly polysemous senses in WordNet. Decision lists are shown a versatile state-of-the-art technique. The experiments reveal, among other facts, that SemCor can be an acceptable (0.7 precision for polysemous words) starting point for an all-words system. The results on the DSO corpus show that for some highly polysemous words 0.7 precision seems to be the current state-of-the-art limit. On the other hand, independently constructed hand-tagged corpora are not mutually useful, and a corpus automatically acquired from the Web is shown to fail.
dc.description9 pages
dc.identifierhttps://arxiv.org/abs/cs/0010024
dc.identifierhttp://arxiv.org/abs/cs/0010024
dc.identifierProcedings of the COLING 2000 Workshop on Semantic Annotation and Intelligent Content
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/30427
dc.subjectComputation and Language
dc.subjectI.2.7
dc.titleExploring automatic word sense disambiguation with decision lists and the Web
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