What is word meaning, really?: (and how can distributional models help us describe it?)

  • Authors:
  • Katrin Erk

  • Affiliations:
  • University of Texas at Austin

  • Venue:
  • GEMS '10 Proceedings of the 2010 Workshop on GEometrical Models of Natural Language Semantics
  • Year:
  • 2010

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Abstract

In this paper, we argue in favor of reconsidering models for word meaning, using as a basis results from cognitive science on human concept representation. More specifically, we argue for a more flexible representation of word meaning than the assignment of a single best-fitting dictionary sense to each occurrence: Either use dictionary senses, but view them as having fuzzy boundaries, and assume that an occurrence can activate multiple senses to different degrees. Or move away from dictionary senses completely, and only model similarities between individual word usages. We argue that distributional models provide a flexible framework for experimenting with alternative models of word meanings, and discuss example models.