Paper: Integrating Multiple Knowledge Sources To Disambiguate Word Sense: An Exemplar-Based Approach

ACL ID P96-1006
Title Integrating Multiple Knowledge Sources To Disambiguate Word Sense: An Exemplar-Based Approach
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1996
Authors

In this paper, we present a new approach for word sense disambiguation (WSD) us- ing an exemplar-based learning algorithm. This approach integrates a diverse set of knowledge sources to disambiguate word sense, including part of speech of neigh- boring words, morphological form, the un- ordered set of surrounding words, local collocations, and verb-object syntactic re- lation. We tested our WSD program, named LEXAS, on both a common data set used in previous work, as well as on a large sense-tagged corpus that we sep- arately constructed. LEXAS achieves a higher accuracy on the common data set, and performs better than the most frequent heuristic on the highly ambiguous words in the large corpus tagged with the refined senses of WoRDNET.