Paper: Statistical Sense Disambiguation With Relatively Small Corpora Using Dictionary Definitions

ACL ID P95-1025
Title Statistical Sense Disambiguation With Relatively Small Corpora Using Dictionary Definitions
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1995
Authors
  • Alpha K. Luk (Microsoft Institute, North Ryde Australia; Macquarie University, Sydney Australia)

Corpus-based sense disambiguation methods, like most other statistical NLP approaches, suffer from the problem of data sparseness. In this paper, we describe an approach which overcomes this problem using dictionary definitions. Using the definition- based conceptual co-occurrence data collected from the relatively small Brown corpus, our sense disambiguation system achieves an average accuracy comparable to human performance given the same contextual information.