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A Maximum Entropy Approach for Spoken Chinese Understanding

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Computational Linguistics and Intelligent Text Processing (CICLing 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2588))

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Abstract

In this paper, we present a spoken language understanding method based on the maximum entropy model. We first extract certain features from the corpus, and then train the maximum entropy model with an annotated corpus. We use this model to analyze spoken Chinese into semantic frames. Experiments show that the model can work effectively.

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Author information

Authors and Affiliations

  1. National Laboratory of Pattern Recognition, Institute of Automation Chinese Academy of Sciences, 100080, Beijing

    Guodong Xie, Chengqing Zong & Bo Xu

Authors
  1. Guodong Xie
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  2. Chengqing Zong
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  3. Bo Xu
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Editor information

Editors and Affiliations

  1. Centro de Investigación en Computación (CIC), Instituto Politécnico Nacional (IPN), Col. Zacatenco, CP 07738, Mexico D.F., Mexico

    Alexander Gelbukh

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© 2003 Springer-Verlag Berlin Heidelberg

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Xie, G., Zong, C., Xu, B. (2003). A Maximum Entropy Approach for Spoken Chinese Understanding. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2003. Lecture Notes in Computer Science, vol 2588. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36456-0_10

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  • DOI: https://doi.org/10.1007/3-540-36456-0_10

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00532-2

  • Online ISBN: 978-3-540-36456-6

  • eBook Packages: Springer Book Archive

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