Combining Unsupervised Lexical Knowledge Methods for Word Sense Disambiguation

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dc.creatorRigau, German
dc.creatorAtserias, Jordi
dc.creatorAgirre, Eneko
dc.date1997-04-21
dc.date.accessioned2026-07-07T09:10:46Z
dc.date.available2026-07-07T09:10:46Z
dc.descriptionThis paper presents a method to combine a set of unsupervised algorithms that can accurately disambiguate word senses in a large, completely untagged corpus. Although most of the techniques for word sense resolution have been presented as stand-alone, it is our belief that full-fledged lexical ambiguity resolution should combine several information sources and techniques. The set of techniques have been applied in a combined way to disambiguate the genus terms of two machine-readable dictionaries (MRD), enabling us to construct complete taxonomies for Spanish and French. Tested accuracy is above 80% overall and 95% for two-way ambiguous genus terms, showing that taxonomy building is not limited to structured dictionaries such as LDOCE.
dc.description8 pages, uses aclap.sty
dc.identifierhttps://arxiv.org/abs/cmp-lg/9704007
dc.identifierhttp://arxiv.org/abs/cmp-lg/9704007
dc.identifierProceedings of ACL'97
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/151446
dc.subjectComputation and Language
dc.titleCombining Unsupervised Lexical Knowledge Methods for Word Sense Disambiguation
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