Discovering word senses from text

  • Authors:

  • Patrick Pantel;Dekang Lin

  • Affiliations:

  • University of Alberta, Edmonton, Alberta, Canada;University of Alberta, Edmonton, Alberta, Canada

  • Venue:
  • Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
  • Year:
  • 2002

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Abstract

Inventories of manually compiled dictionaries usually serve as a source for word senses. However, they often include many rare senses while missing corpus/domain-specific senses. We present a clustering algorithm called CBC (Clustering By Committee) that automatically discovers word senses from text. It initially discovers a set of tight clusters called committees that are well scattered in the similarity space. The centroid of the members of a committee is used as the feature vector of the cluster. We proceed by assigning words to their most similar clusters. After assigning an element to a cluster, we remove their overlapping features from the element. This allows CBC to discover the less frequent senses of a word and to avoid discovering duplicate senses. Each cluster that a word belongs to represents one of its senses. We also present an evaluation methodology for automatically measuring the precision and recall of discovered senses.