Statistical metaphor processing

  • Authors:
  • Ekaterina Shutova;Simone Teufel;Anna Korhonen

  • Affiliations:
  • University of Cambridge;University of Cambridge;University of Cambridge

  • Venue:
  • Computational Linguistics
  • Year:
  • 2013

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Abstract

Metaphor is highly frequent in language, which makes its computational processing indispensable for real-world NLP applications addressing semantic tasks. Previous approaches to metaphor modeling rely on task-specific hand-coded knowledge and operate on a limited domain or a subset of phenomena. We present the first integrated open-domain statistical model of metaphor processing in unrestricted text. Our method first identifies metaphorical expressions in running text and then paraphrases them with their literal paraphrases. Such a text-to-text model of metaphor interpretation is compatible with other NLP applications that can benefit from metaphor resolution. Our approach is minimally supervised, relies on the state-of-the-art parsing and lexical acquisition technologies distributional clustering and selectional preference induction, and operates with a high accuracy.