Using semantically motivated estimates to help subcategorization acquisition

  • Authors:
  • Anna Korhonen

  • Affiliations:
  • University of Cambridge, Cambridge, UK

  • Venue:
  • EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
  • Year:
  • 2000

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Abstract

Research into the automatic acquisition of subcategorization frames from corpora is starting to produce large-scale computational lexicons which include valuable frequency information. However, the accuracy of the resulting lexicons shows room for improvement. One source of error lies in the lack of accurate back-off estimates for subcategorization frames, delimiting the performance of statistical techniques frequently employed in verbal acquisition. In this paper, we propose a method of obtaining more accurate, semantically motivated back-off estimates, demonstrate how these estimates can be used to improve the learning of subcategorization frames, and discuss using the method to benefit large-scale lexical acquisition.