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A Nearest-Neighbor Method for Resolving PP-Attachment Ambiguity

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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

We present a nearest-neighbor algorithm for resolving prepositional phrase attachment ambiguities. Its performance is significantly higher than previous corpus-based methods for PP-attachment that do not rely on manually constructed knowledge bases. We will also show that the PP-attachment task provides a way to evaluate methods for computing distributional word similarities. Our experiments indicate that the cosine of pointwise mutual information vector is a significantly better similarity measure than several other commonly used similarity measures.

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Authors and Affiliations

  1. Department of Computing Science, University of Alberta, Edmonton, Alberta, T6G 2E8

    Shaojun Zhao & Dekang Lin

Authors
  1. Shaojun Zhao
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  2. Dekang Lin
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Editor information

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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Zhao, S., Lin, D. (2005). A Nearest-Neighbor Method for Resolving PP-Attachment Ambiguity. 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_58

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  • DOI: https://doi.org/10.1007/978-3-540-30211-7_58

  • 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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