A Study on Feature Weighting in Chinese Text Categorization

  • Xue Dejun
  • Sun Maosong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2588)

Abstract

In Text Categorization (TC) based on Vector Space Model, feature weighting and feature selection are major problems and difficulties. This paper proposes two methods of weighting features by combining the relevant influential factors together. A TC system for Chinese texts is designed in terms of character bigrams as features. Experiments on a document collection of 71,674 texts show that the F1 metric of categorization performance of the system is 85.9%, which is about 5% higher than that of the well-known TF*IDF weighting scheme. Moreover, a multi-step feature selection process is exploited to reduce the dimension of the feature space effectively in the system.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Xue Dejun
    • 1
  • Sun Maosong
    • 1
  1. 1.National Key Laboratory of Intelligent Technology and Systems Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

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