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Automatic Learning of Parallel Dependency Treelet Pairs

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Natural Language Processing – IJCNLP 2004 (IJCNLP 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3248))

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Abstract

Induction of synchronous grammars from empirical data has long been an unsolved problem; despite generative synchronous grammars theoretically suit the machine translation task very well. This fact is mainly due to pervasive structural divergences between languages. This paper presents a statistical approach that learns dependency structure mappings from parallel corpora. The new algorithm automatically learns parallel dependency treelet pairs from loosely matched non-isomorphic dependency trees while keeping computational complexity polynomial in the length of the sentences. A set of heuristics is introduced and specifically optimized for parallel treelet learning purposes using Minimum Error Rate training.

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Author information

Authors and Affiliations

  1. Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, 19104, USA

    Yuan Ding & Martha Palmer

Authors
  1. Yuan Ding
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  2. Martha Palmer
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Editors and Affiliations

  1. Behavior Design Corporation, IV Science-Based Industrial Park Hsinchu, 2F, No.5, Industry E. Rd, Taiwan

    Keh-Yih Su

  2. University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, JST CREST, Honcho 4-1-8, Kawaguchi-shi,, 332-0012, Saitama,  

    Jun’ichi Tsujii

  3. Pohang University of Science and Technology (POSTECH), AITrc, Republic of Korea

    Jong-Hyeok Lee

  4. Language Information Sciences Research Centre, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong

    Oi Yee Kwong

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© 2005 Springer-Verlag Berlin Heidelberg

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Ding, Y., Palmer, M. (2005). Automatic Learning of Parallel Dependency Treelet Pairs. In: Su, KY., Tsujii, J., Lee, JH., Kwong, O.Y. (eds) Natural Language Processing – IJCNLP 2004. IJCNLP 2004. Lecture Notes in Computer Science(), vol 3248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30211-7_25

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  • DOI: https://doi.org/10.1007/978-3-540-30211-7_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24475-2

  • Online ISBN: 978-3-540-30211-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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