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Proceedings of The Eighth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 2013 pp 217–224Cite as

Automatic Text Classification Based on Hidden Markov Model and Support Vector Machine

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 212))

Abstract

This paper researches the technology of text classification, and proposes a two-layer structure automatic text classification based on HMM and SVM. The given text is classified with HMM classifiers first to select the most likely two classes. Then these classes as SVM input are processed. Finally the given text is classified into the corresponding category with SVM classifier. The experimental results show that this method is more efficient for text classification in recognition ratio.

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References

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Acknowledgments

This work is supported by the provincial natural science research projects (KJ2011Z097) of Anhui.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, Anhui, 232001, China

    Li Wang & Li Li

Authors
  1. Li Wang
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  2. Li Li
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Corresponding author

Correspondence to Li Wang .

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Editors and Affiliations

  1. School of Science, Anhui University of Science & Technology, Huainan, China

    Zhixiang Yin

  2. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  3. Anhui University of Science & Technology, Huainan, China

    Xianwen Fang

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© 2013 Springer-Verlag Berlin Heidelberg

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Wang, L., Li, L. (2013). Automatic Text Classification Based on Hidden Markov Model and Support Vector Machine. In: Yin, Z., Pan, L., Fang, X. (eds) Proceedings of The Eighth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 2013. Advances in Intelligent Systems and Computing, vol 212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37502-6_27

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  • DOI: https://doi.org/10.1007/978-3-642-37502-6_27

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37501-9

  • Online ISBN: 978-3-642-37502-6

  • eBook Packages: EngineeringEngineering (R0)

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