Towards a self-extending lexicon

  • Authors:
  • Uri Zernik;Michael G. Dyer

  • Affiliations:
  • University of California, Los Angeles, California;University of California, Los Angeles, California

  • Venue:
  • ACL '85 Proceedings of the 23rd annual meeting on Association for Computational Linguistics
  • Year:
  • 1985

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Abstract

The problem of manually modifying the lexicon appears with any natural language processing program. Ideally, a program should be able to acquire new lexical entries from context, the way people learn. We address the problem of acquiring entire phrases, specifically figurative phrases, through augmenting a phrasal lexicon. Facilitating such a self-extending lexicon involves (a) disambiguation---selection of the intended phrase from a set of matching phrases, (b) robust parsing---comprehension of partially-matching phrases, and (c) error analysis---use of errors in forming hypotheses about new phrases. We have designed and implemented a program called RINA which uses demons to implement functional-grammar principles. RINA receives new figurative phrases in context and through the application of a sequence of failure-driven rules, creates and refines both the patterns and the concepts which hold syntactic and semantic information about phrases.