skip to main content
10.3115/992730.992781dlproceedingsArticle/Chapter ViewAbstractPublication PagescolingConference Proceedingsconference-collections
Article
Free Access

Acquisition of phrase-level bilingual correspondence using dependency structure

Authors Info & Claims
Published:31 July 2000Publication History

ABSTRACT

This paper describes a method to find phrase-level translation patterns from parallel corpora by applying dependency structure analysis. We use statistical dependency parsers to determine dependency relations between base phrases in a sentence. Our method is tested with a business expression corpus containing 10000 English-Japanese sentence pairs and achieved approximately 90% accuracy in extracting bilingual correspondences. The result shows that the use of dependency relation helps to acquire interesting translation patterns.

References

  1. P. F. Brown, J. C. Lai, and R. L. Mercer. 1991. Aligning sentences in parallel corpora. In ACL-29:29th Annual Meeting of the Association for Computational Linguistics, pages 169--176. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. E. Charniak. 2000. A maximum-entropy-inspired parser. In NAACL-2000: 1st Meeting of the North American Chapter of the Association for Computational Linguistics, pages 132--139. Google ScholarGoogle Scholar
  3. M. J. Collins. 1997. Three generative, lexicalised models for statistical parsing. In ACL-97: 35th Annual Meeting of the Association for Computational Linguistics, pages 16--23. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. I. Dagan, K. Church, and W. Gale. 1992. Robust bilingual word alignment for machine aided translation. In Proc. of the Workshop on Very Large Corpora, pages 1--8.Google ScholarGoogle Scholar
  5. M. Fujio and Y. Matsumoto. 1998. Japanese dependency structure analysis based on lexicalized statistics. In Proc. of 3rd Conf. on Emperical Methods in Natural Language Processing, pages 88--96.Google ScholarGoogle Scholar
  6. R. Hudson. 1984. Word Grammar. Blackwell.Google ScholarGoogle Scholar
  7. M. Kitamura and Y. Matsumoto. 1995. A machine translation system based on translation rules acquired from parallel corpora. In Proc. of Recent Advances in Natural Lannguage Processing, pages 27--44.Google ScholarGoogle Scholar
  8. M. Kitamura and Y. Matsumoto. 1996. Automatic extraction of word sequence correspondences in parallel corpora. In Proc. 4th Workshop on Very Large Corpora, pages 79--87.Google ScholarGoogle Scholar
  9. Y. Matsumoto, H. Ishimoto, and T. Utsuro. 1993. Structural matching of parallel texts. In ACL-93: 31st Annual Meeting of the Association for Computational Linguistics, pages 23--30. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. I. D. Melamed. 1995. Automatic evaluation and uniform filter cascades for inducing n-best translation lexicons. In Proc. of 3rd Workshop on Very Large Corpora, pages 184--198.Google ScholarGoogle Scholar
  11. A. Ratnaparkhi. 1997. A linear observed time statistical parser based on maximum entropy models. In Proc. of 2nd Conf. on Emperical Methods in Natural Language Processing, pages 1--10.Google ScholarGoogle Scholar
  12. K. Takubo and M. Hashimoto. 1995. A Dictionary of English Business Letter Expressions. Nihon Keizai Shimbun, Inc.Google ScholarGoogle Scholar
  1. Acquisition of phrase-level bilingual correspondence using dependency structure

        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 2
          July 2000
          549 pages

          Publisher

          Association for Computational Linguistics

          United States

          Publication History

          • Published: 31 July 2000

          Qualifiers

          • Article

          Acceptance Rates

          Overall Acceptance Rate1,537of1,537submissions,100%

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader