A decision tree of bigrams is an accurate predictor of word sense

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Abstract

This paper presents a corpus-based approach to word sense disambiguation where a decision tree assigns a sense to an ambiguous word based on the bigrams that occur nearby. This approach is evaluated using the sense-tagged corpora from the 1998 SENSEVAL word sense disambiguation exercise. It is more accurate than the average results reported for 30 of 36 words, and is more accurate than the best results for 19 of 36 words.

Original languageEnglish (US)
StatePublished - 2001
Event2nd Meeting of the North American Chapter of the Association for Computational Linguistics, NAACL 2001 - Pittsburgh, United States
Duration: Jun 2 2001Jun 7 2001

Conference

Conference2nd Meeting of the North American Chapter of the Association for Computational Linguistics, NAACL 2001
Country/TerritoryUnited States
CityPittsburgh
Period6/2/016/7/01

Bibliographical note

Publisher Copyright:
© 2nd Meeting of the North American Chapter of the Association for Computational Linguistics, NAACL 2001. All rights reserved.

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