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ACL
1997

Combining Unsupervised Lexical Knowledge Methods for Word Sense Disambiguation

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Combining Unsupervised Lexical Knowledge Methods for Word Sense Disambiguation
This 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- edged 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 taxonomybuilding is not limitedto structured dictionaries such as LDOCE.
German Rigau, Jordi Atserias, Eneko Agirre
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 1997
Where ACL
Authors German Rigau, Jordi Atserias, Eneko Agirre
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