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SAW Classification Algorithm for Chinese Text Classification

Author

Listed:
  • Xiaoli Guo

    (School of Information Engineering, Northeast Dianli University, Jilin 132012, China)

  • Huiyu Sun

    (School of Information Engineering, Northeast Dianli University, Jilin 132012, China)

  • Tiehua Zhou

    (Database/Bioinformatics Laboratory, Chungbuk National University, Chungbuk 362-763, Korea)

  • Ling Wang

    (School of Information Engineering, Northeast Dianli University, Jilin 132012, China)

  • Zhaoyang Qu

    (School of Information Engineering, Northeast Dianli University, Jilin 132012, China)

  • Jiannan Zang

    (School of Information Engineering, Northeast Dianli University, Jilin 132012, China)

Abstract

Considering the explosive growth of data, the increased amount of text data’s effect on the performance of text categorization forward the need for higher requirements, such that the existing classification method cannot be satisfied. Based on the study of existing text classification technology and semantics, this paper puts forward a kind of Chinese text classification oriented SAW (Structural Auxiliary Word) algorithm. The algorithm uses the special space effect of Chinese text where words have an implied correlation between text information mining and text categorization for high-correlation matching. Experiments show that SAW classification algorithm on the premise of ensuring precision in classification, significantly improve the classification precision and recall, obviously improving the performance of information retrieval, and providing an effective means of data use in the era of big data information extraction.

Suggested Citation

  • Xiaoli Guo & Huiyu Sun & Tiehua Zhou & Ling Wang & Zhaoyang Qu & Jiannan Zang, 2015. "SAW Classification Algorithm for Chinese Text Classification," Sustainability, MDPI, vol. 7(3), pages 1-15, February.
    • Handle: RePEc:gam:jsusta:v:7:y:2015:i:3:p:2338-2352:d:46200
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    Citations

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    Cited by:

    1. Taeyeoun Roh & Yujin Jeong & Byungun Yoon, 2017. "Developing a Methodology of Structuring and Layering Technological Information in Patent Documents through Natural Language Processing," Sustainability, MDPI, vol. 9(11), pages 1-19, November.
    2. Ling Wang & Gongliang Hu & Tiehua Zhou, 2018. "Semantic Analysis of Learners’ Emotional Tendencies on Online MOOC Education," Sustainability, MDPI, vol. 10(6), pages 1-19, June.

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