skip to main content
10.3115/990820.990849dlproceedingsArticle/Chapter ViewAbstractPublication PagescolingConference Proceedingsconference-collections
Article
Free Access

A class-based probabilistic approach to structural disambiguation

Published:31 July 2000Publication History

ABSTRACT

Knowledge of which words are able to fill particular argument slots of a predicate can be used for structural disambiguation. This paper describes a proposal for acquiring such knowledge, and in line with much of the recent work in this area, a probabilistic approach is taken. We develop a novel way of using a semantic hierarchy to estimate the probabilities, and demonstrate the general approach using a prepositional phrase attachment experiment.

References

  1. Eric Brill and Philip Resnik. 1994. A rule-based approach to prepositional phrase attachment disambiguation. In Proceedings of the fifteenth International Conference on Computational Linguistics. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Eugene Charniak. 1993. Statistical Language Learning. The MIT Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Stephen Clark and David Weir. 1999. An iterative approach to estimating frequencies over a semantic hierarchy. In Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, pages 258--265.Google ScholarGoogle Scholar
  4. Michael Collius. 1995. Prepositional phrase attachment through a backed-off model. In Proceedings of the Third Workshop on Very Large Corpora, pages 27--38, Cambridge, Massachusetts.Google ScholarGoogle Scholar
  5. Michael Collins. 1996. A new statistical parser based on bigram lexical dependencies. In Proceedings of the 34th Annual Meeting of the ACL, pages 184--191. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Michael Collins. 1997. Three generative, lexicalised models for statistical parsing. In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics, pages 16--23. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Ted Dunning. 1993. Accurate methods for the statistics of surprise and coincidence. Computational Linguistics, 19(1):61--74. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Christiane Fellbaum, editor. 1998. WordNet An Electronic Lexical Database. The MIT Press.Google ScholarGoogle Scholar
  9. Donald Hindle and Mats Rooth. 1993. Structural ambiguity and lexical relations. Computational Linguistics, 19(1):103--120. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. David Howell. 1997. Statistical Methods for Psychology: 4th ed. Duxbury Press.Google ScholarGoogle Scholar
  11. Hang Li and Naoki Abe. 1998. Generalizing case frames using a thesaurus and the MDL principle. Computational Linguistics, 24(2):217--244. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Adwait Ratnaparkhi, Jeff Reynar, and Salim Roukos. 1994. A maximum cntropy model for prepositional phrase attachment. In Proceedings of the ARPA Human Language Technology Workshop, pages 250--255. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Adwait Ratnaparkhi. 1998. Unsupervised statistical models for prepositional phrase attachment. In Proceedings of the Seventeenth International Conference on Computational Linguistics, Montreal, Canada, Aug. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Philip Resnik. 1993. Selection and Information: A Class-Based Approach to Lexical Relationships. Ph. D. thesis, University of Pennsylvania. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Francesc Ribas. 1995. On learning more appropriate selectional restrictions. In Proceedings of the Seventh Conference of the European Chapter of the Association for Computational Linguistics, Dublin, Ireland. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Jiri Stetina and Makoto Nagao. 1997. Corpus based PP attachment ambiguity resolution with a semantic dictionary. In Proceedings of the Fifth Workshop on Very Large Corpora, pages 66--80, Beijing and Hong Kong.Google ScholarGoogle Scholar
  17. David Yarowsky. 1992. Word-sense disambiguation using statistical models of Roget's categories trained on large corpora. In Proceedings of COLING-92, pages 454--460. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Jakub Zavrel and Walter Daelemans. 1997. Memory-based learning: Using similarity for smoothing. In Proceeding of ACL/EACL-97, Madrid, Spain. Google ScholarGoogle ScholarDigital LibraryDigital Library
  1. A class-based probabilistic approach to structural disambiguation

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image DL Hosted proceedings
        COLING '00: Proceedings of the 18th conference on Computational linguistics - Volume 1
        July 2000
        616 pages
        ISBN:155860717X

        Publisher

        Association for Computational Linguistics

        United States

        Publication History

        • Published: 31 July 2000

        Qualifiers

        • Article

        Acceptance Rates

        Overall Acceptance Rate1,537of1,537submissions,100%
      • Article Metrics

        • Downloads (Last 12 months)26
        • Downloads (Last 6 weeks)3

        Other Metrics

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader