Using syntactic dependency as local context to resolve word sense ambiguity

  • Authors:
  • Dekang Lin

  • Affiliations:
  • University of Manitoba, Winnipeg, Manitoba, Canada

  • Venue:
  • ACL '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
  • Year:
  • 1997

Quantified Score

Hi-index 0.00

Visualization

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.