Word Sense Disambiguation using Optimised Combinations of Knowledge Sources

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dc.creatorWilks, Yorick
dc.creatorStevenson, Mark
dc.date1998-06-22
dc.date.accessioned2026-07-07T02:36:16Z
dc.date.available2026-07-07T02:36:16Z
dc.descriptionWord sense disambiguation algorithms, with few exceptions, have made use of only one lexical knowledge source. We describe a system which performs unrestricted word sense disambiguation (on all content words in free text) by combining different knowledge sources: semantic preferences, dictionary definitions and subject/domain codes along with part-of-speech tags. The usefulness of these sources is optimised by means of a learning algorithm. We also describe the creation of a new sense tagged corpus by combining existing resources. Tested accuracy of our approach on this corpus exceeds 92%, demonstrating the viability of all-word disambiguation rather than restricting oneself to a small sample.
dc.description7 pages, uses colacl.sty. To appear in the Proceedings of COLING-ACL '98
dc.identifierhttps://arxiv.org/abs/cmp-lg/9806014
dc.identifierhttp://arxiv.org/abs/cmp-lg/9806014
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/15858
dc.subjectComputation and Language
dc.titleWord Sense Disambiguation using Optimised Combinations of Knowledge Sources
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