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

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Advances in Information Retrieval (ECIR 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2633))

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Abstract

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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Authors and Affiliations

  1. State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing, China, 100084

    Xue Dejun & Sun Maosong

Authors
  1. Xue Dejun
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  2. Sun Maosong
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Editors and Affiliations

  1. Instituto di Scienza e Tecnologie dell’Informazione, Consiglio Nazionale delle Ricerche, Via Giuseppe Moruzzi, 1, 56124, Pisa, Italy

    Fabrizio Sebastiani

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Dejun, X., Maosong, S. (2003). Chinese Text Categorization Based on the Binary Weighting Model with Non-binary Smoothing. In: Sebastiani, F. (eds) Advances in Information Retrieval. ECIR 2003. Lecture Notes in Computer Science, vol 2633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36618-0_29

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  • DOI: https://doi.org/10.1007/3-540-36618-0_29

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-01274-0

  • Online ISBN: 978-3-540-36618-8

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