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Learning Chinese Polarity Lexicons by Integration of Graph Models and Morphological Features

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Information Retrieval Technology (AIRS 2010)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6458))

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Abstract

This paper presents a novel way to learn Chinese polarity lexicons by using both external relations and internal formation of Chinese words, i.e. by integrating two kinds of different but complementary models: graph models and morphological feature-based models. The polarity detection is first treated as a semi-supervised learning in a graph, and then machine learning is used based on morphological features of Chinese words. The results show that the the integration of morphological feature-based models and graph models significantly outperforms the baselines.

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Author information

Authors and Affiliations

  1. Department of Chinese, Translation & Linguistics, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong

    Bin Lu, Yan Song, Xing Zhang & Benjamin K. Tsou

  2. Research Centre on Linguistics and Language Information Sciences, Hong Kong Institute of Education, Tai Po, New Territories, Hong Kong

    Benjamin K. Tsou

Authors
  1. Bin Lu
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  2. Yan Song
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  3. Xing Zhang
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  4. Benjamin K. Tsou
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Information Engineering, Roosevelt Road National Taiwan University, No. 1, Sec. 4, 10617, Taipei, Taiwan R.O.C.

    Pu-Jen Cheng

  2. School of Computing, National University of Singapore (NUS), Computing 1, 13 Computing Drive, 117417, Singapore

    Min-Yen Kan

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

    Wai Lam

  4. School of Computing, Computing 1, National University of Singapore (NUS), 13 Computing Drive, 117417, Singapore

    Preslav Nakov

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© 2010 Springer-Verlag Berlin Heidelberg

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Lu, B., Song, Y., Zhang, X., Tsou, B.K. (2010). Learning Chinese Polarity Lexicons by Integration of Graph Models and Morphological Features. In: Cheng, PJ., Kan, MY., Lam, W., Nakov, P. (eds) Information Retrieval Technology. AIRS 2010. Lecture Notes in Computer Science, vol 6458. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17187-1_45

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  • DOI: https://doi.org/10.1007/978-3-642-17187-1_45

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17186-4

  • Online ISBN: 978-3-642-17187-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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