Chinese Text Categorization Based on the Binary Weighting Model with Non-binary Smoothing

  • Xue Dejun
  • Sun Maosong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2633)


In Text Categorization (TC) based on the vector space model, feature weighting is vital for the categorization effectiveness. Various non-binary weighting schemes are widely used for this purpose. By emphasizing the category discrimination capability of features, the paper firstly puts forward a new weighting scheme TF*IDF*IG. Upon the fact that refined statistics may have more chance to meet sparse data problem, we re-evaluate the role of the Binary Weighting Model (BWM) in TC for further consideration. As a consequence, a novel approach named the Binary Weighting Model with Non-Binary Smoothing (BWM-NBS) is then proposed so as to overcome the drawback of BWM. A TC system for Chinese texts using words as features is implemented. Experiments on a large-scale Chinese document collection with 71,674 texts show that the F1 metric of categorization performance of BWM-NBS gets to 94.9% in the best case, which is 26.4% higher than that of TF*IDF, 19.1% higher than that of TF*IDF*IG, and 5.8% higher than that of BWM under the same condition. Moreover, BWM-NBS exhibits the strong stability in categorization performance.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

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

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