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A Comparative Study of Language Models for Book and Author Recognition

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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

Linguistic information can help improve evaluation of similarity between documents; however, the kind of linguistic information to be used depends on the task. In this paper, we show that distributions of syntactic structures capture the way works are written and accurately identify individual books more than 76% of the time. In comparison, baseline features, e.g., tfidf-weighted keywords, function words, etc., give an accuracy of at most 66%. However, testing the same features on authorship attribution shows that distributions of syntactic structures are less successful than function words on this task; syntactic structures vary even among the works of the same author whereas features such as function words are distributed more similarly among the works of an author and can more effectively capture authorship.

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Author information

Authors and Affiliations

  1. Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, 02139

    Özlem Uzuner & Boris Katz

Authors
  1. Özlem Uzuner
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  2. Boris Katz
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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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Uzuner, Ö., Katz, B. (2005). A Comparative Study of Language Models for Book and Author Recognition. 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_84

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  • DOI: https://doi.org/10.1007/11562214_84

  • 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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