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A Multi-stage Chinese Collocation Extraction System

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Advances in Machine Learning and Cybernetics

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3930))

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Abstract

Most of the existing collocation extraction systems are based on globally significant statistical behaviors without mechanisms to handle different types of collocations. By taking compositionality, substitutability, modifiability and internal associations into consideration, collocations are categorized into four different types in this work. Based on the analysis for each type of collocation, a multi-stage extraction system is designed using different combinations of discriminative features so as to identify different types of collocations in different stages. Perceptron training is employed to optimize the consolidation of discriminative features from different sources. Experiment results show that the achieved performance is much better than most reported work.

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Authors and Affiliations

  1. Dept. of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

    Ruifeng Xu & Qin Lu

Authors
  1. Ruifeng Xu
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  2. Qin Lu
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Editors and Affiliations

  1. Department of Computing, Hong Kong Polytechnic University, P.O. Box, Hong Kong, China

    Daniel S. Yeung

  2. School of Creative Media, City University of Hong Kong,, China

    Zhi-Qiang Liu

  3. Department of Mathematics and Computer Science, Hebei University, 071002, Baoding, Hebei, P.R. China

    Xi-Zhao Wang

  4. School of Electrical and Information Engineering, University of Sydney, 2006, NSW, Australia

    Hong Yan

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© 2006 Springer-Verlag Berlin Heidelberg

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Xu, R., Lu, Q. (2006). A Multi-stage Chinese Collocation Extraction System. In: Yeung, D.S., Liu, ZQ., Wang, XZ., Yan, H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science(), vol 3930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11739685_77

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  • DOI: https://doi.org/10.1007/11739685_77

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33584-9

  • Online ISBN: 978-3-540-33585-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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