Distributional measures of concept-distance: a task-oriented evaluation

  • Authors:
  • Saif Mohammad;Graeme Hirst

  • Affiliations:
  • University of Toronto, Toronto, ON, Canada;University of Toronto, Toronto, ON, Canada

  • Venue:
  • EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
  • Year:
  • 2006

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Abstract

We propose a framework to derive the distance between concepts from distributional measures of word co-occurrences. We use the categories in a published thesaurus as coarse-grained concepts, allowing all possible distance values to be stored in a concept--concept matrix roughly .01% the size of that created by existing measures. We show that the newly proposed concept-distance measures outperform traditional distributional word-distance measures in the tasks of (1) ranking word pairs in order of semantic distance, and (2) correcting real-word spelling errors. In the latter task, of all the WordNet-based measures, only that proposed by Jiang and Conrath outperforms the best distributional concept-distance measures.