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Feature Selection on Chinese Text Classification Using Character N-Grams

  • Zhihua Wei
  • Duoqian Miao
  • Jean-Hugues Chauchat
  • Caiming Zhong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5009)

Abstract

In this paper, we perform Chinese text classification using n-gram text representation on TanCorp which is a new large corpus special for Chinese text classification more than 14,000 texts divided into 12 classes. We use different n-gram feature (1-, 2-grams or 1-, 2-, 3-grams) to represent documents. Different feature weights (absolute text frequency, relative text frequency, absolute n-gram frequency and relative n-gram frequency) are compared. The sparseness of “document by feature” matrices is analyzed in various cases. We use the C-SVC classifier which is the SVM algorithm designed for the multi-classification task. We perform our experiments in the TANAGRA platform. We found out that the feature selection methods based on n-gram frequency (absolute or relative) always give better results and produce denser matrices.

Keywords

Chinese text classification N-gram Feature selection 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Zhihua Wei
    • 1
    • 2
  • Duoqian Miao
    • 1
  • Jean-Hugues Chauchat
    • 2
  • Caiming Zhong
    • 1
  1. 1.Key laboratory “Embedded System and Service Computing” Ministry of EducationTongji UniversityShanghaiChina
  2. 2.Laboratoire ERICUniversité Lumière Lyon 2Bron CedexFrance

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