Class-driven attribute extraction

  • Authors:
  • Benjamin Van Durme;Ting Qian;Lenhart Schubert

  • Affiliations:
  • University of Rochester, Rochester, NY;University of Rochester, Rochester, NY;University of Rochester, Rochester, NY

  • Venue:
  • COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
  • Year:
  • 2008

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Abstract

We report on the large-scale acquisition of class attributes with and without the use of lists of representative instances, as well as the discovery of unary attributes, such as typically expressed in English through prenominal adjectival modification. Our method employs a system based on compositional language processing, as applied to the British National Corpus. Experimental results suggest that document-based, open class attribute extraction can produce results of comparable quality as those obtained using web query logs, indicating the utility of exploiting explicit occurrences of class labels in text.