Evaluating WordNet-based Measures of Lexical Semantic Relatedness

  • Authors:
  • Alexander Budanitsky;Graeme Hirst

  • Affiliations:
  • Department of Computer Science, University of Toronto, Toronto, Ontario, Canada M5S 3G4abm@cs.toronto.edu;Department of Computer Science, University of Toronto, Toronto, Ontario, Canada M5S 3G4abm@cs.toronto.edu

  • Venue:
  • Computational Linguistics
  • Year:
  • 2006

Quantified Score

Hi-index 0.01

Visualization

Abstract

The quantification of lexical semantic relatedness has many applications in NLP, and many different measures have been proposed. We evaluate five of these measures, all of which use WordNet as their central resource, by comparing their performance in detecting and correcting real-word spelling errors. An information-content-based measure proposed by Jiang and Conrath is found superior to those proposed by Hirst and St-Onge, Leacock and Chodorow, Lin, and Resnik. In addition, we explain why distributional similarity is not an adequate proxy for lexical semantic relatedness.