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Automatic Term Extraction Based on Perplexity of Compound Words

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Natural Language Processing – IJCNLP 2005 (IJCNLP 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3651))

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Abstract

Many methods of term extraction have been discussed in terms of their accuracy on huge corpora. However, when we try to apply various methods that derive from frequency to a small corpus, we may not be able to achieve sufficient accuracy because of the shortage of statistical information on frequency. This paper reports a new way of extracting terms that is tuned for a very small corpus. It focuses on the structure of compound terms and calculates perplexity on the term unit’s left-side and right-side. The results of our experiments revealed that the accuracy with the proposed method was not that advantageous. However, experimentation with the method combining perplexity and frequency information obtained the highest average-precision in comparison with other methods.

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Author information

Authors and Affiliations

  1. Information Technology Center, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033

    Minoru Yoshida & Hiroshi Nakagawa

  2. CREST, JST, Honcho 4-1-8, Kawaguchi-shi, Saitama, 332-0012

    Minoru Yoshida & Hiroshi Nakagawa

Authors
  1. Minoru Yoshida
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  2. Hiroshi Nakagawa
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Editor information

Editors and Affiliations

  1. Center for Language Technology, Macquarie University, 2019, Sydney, NSW, Australia

    Robert Dale

  2. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Kam-Fai Wong

  3. Institute for Infocomm Research, 21, Heng Mui Keng Terrace, 119613, Singapore

    Jian Su

  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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Yoshida, M., Nakagawa, H. (2005). Automatic Term Extraction Based on Perplexity of Compound Words. In: Dale, R., Wong, KF., Su, J., Kwong, O.Y. (eds) Natural Language Processing – IJCNLP 2005. IJCNLP 2005. Lecture Notes in Computer Science(), vol 3651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11562214_24

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  • DOI: https://doi.org/10.1007/11562214_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29172-5

  • Online ISBN: 978-3-540-31724-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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