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Using syntactic dependency as local context to resolve word sense ambiguity

Published: 07 July 1997 Publication History

Abstract

Most previous corpus-based algorithms disambiguate a word with a classifier trained from previous usages of the same word. Separate classifiers have to be trained for different words. We present an algorithm that uses the same knowledge sources to disambiguate different words. The algorithm does not require a sense-tagged corpus and exploits the fact that two different words are likely to have similar meanings if they occur in identical local contexts.

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      cover image DL Hosted proceedings
      ACL '98/EACL '98: Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
      July 1997
      543 pages

      Sponsors

      • Directorate General XIII (European Commission)
      • Universidad Complutense de Madrid
      • Universidad Autónoma de Madrid
      • Universidad Nacional de Educación a Distancia
      • Universidad Politécnica de Madrid

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      Association for Computational Linguistics

      United States

      Publication History

      Published: 07 July 1997

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