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International Symposium on Natural Language Processing Based on Naturally Annotated Big Data

China National Conference on Computational Linguistics

NLP-NABD 2016, CCL 2016: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data pp 114–124Cite as

Improved Joint Kazakh POS Tagging and Chunking

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10035))

Abstract

This paper describes a mixing model of joint POS tagging and chunking for Kazakh where partial optimal solution provide feature information for joint model. A improved beam-search algorithm use dynamic beam instead of unified beam to obtain search space of small-but-excellent during both training and decoding phases of the model. Moreover we can statistical induction the information of chunk to disambiguation of multi-category words and experiment shows the precision is improved from 81.6 % to 87.7 % by information of chunk.

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Author information

Authors and Affiliations

  1. College of Information Science and Engineering, Xinjiang University, Urumqi, China

    Hao Wu & Gulila Altenbek

  2. The Base of Kazakh and Kirghiz Language of National Language Resource Monitoring and Research Centre Minority Languages, Urumqi, China

    Hao Wu & Gulila Altenbek

Authors
  1. Hao Wu
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  2. Gulila Altenbek
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Corresponding authors

Correspondence to Hao Wu or Gulila Altenbek .

Editor information

Editors and Affiliations

  1. Tsinghua University , Beijing, China

    Maosong Sun

  2. Fudan University , Shanghai, China

    Xuanjing Huang

  3. Dalian University of Technology , Dalian, China

    Hongfei Lin

  4. Tsinghua University , Beijing, China

    Zhiyuan Liu

  5. Tsinghua University , Beijing, China

    Yang Liu

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© 2016 Springer International Publishing AG

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Cite this paper

Wu, H., Altenbek, G. (2016). Improved Joint Kazakh POS Tagging and Chunking. In: Sun, M., Huang, X., Lin, H., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2016 2016. Lecture Notes in Computer Science(), vol 10035. Springer, Cham. https://doi.org/10.1007/978-3-319-47674-2_10

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  • DOI: https://doi.org/10.1007/978-3-319-47674-2_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47673-5

  • Online ISBN: 978-3-319-47674-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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