Unsupervised Graph-basedWord Sense Disambiguation Using Measures of Word Semantic Similarity

  • Authors:
  • Ravi Sinha;Rada Mihalcea

  • Affiliations:
  • University of North Texas, USA;University of North Texas, USA

  • Venue:
  • ICSC '07 Proceedings of the International Conference on Semantic Computing
  • Year:
  • 2007

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Abstract

This paper describes an unsupervised graph-based method for word sense disambiguation, and presents comparative evaluations using several measures of word semantic similarity and several algorithms for graph centrality. The results indicate that the right combination of similarity metrics and graph centrality algorithms can lead to a performance competing with the state-of-the-art in unsupervised word sense disambiguation, as measured on standard data sets.