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Supervised and Traditional Term Weighting Methods for Automatic Text Categorization

Supervised and Traditional Term Weighting Methods for Automatic Text Categorization. Presenter : Cheng-Han Tsai Authors : Man Lan , Chew Lim Tan, Senior Member, IEEE, Jian Su, and Yue Lu, Member, IEEE TPAMI, 2009. Outlines. Motivation Objectives Methodology Experiments

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Supervised and Traditional Term Weighting Methods for Automatic Text Categorization

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  1. Supervised and Traditional Term Weighting Methods for Automatic Text Categorization Presenter : Cheng-Han Tsai Authors : Man Lan, Chew Lim Tan, Senior Member, IEEE, Jian Su, and Yue Lu, Member, IEEE TPAMI, 2009

  2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

  3. Motivation Text categorization The popularly used tf‧idf method has not shown a uniformly good performance in terms of different data sets

  4. Objectives Text categorization • To propose a new simple supervised term weighting method to improve the terms’ discriminating power for text categorization task • Are supervised term weighting methods better performance than unsupervised ones for TC? • Does the difference between supervised and unsupervised have any relationship with different learning algorithms? • Why is the new supervised method, i.e., tf‧rf, effective for TC?

  5. Methodology Text categorization TF‧RF

  6. Methodology

  7. Methodology

  8. Methodology

  9. Experiments

  10. Experiments

  11. Experiments

  12. Experiments

  13. Experiments

  14. Conclusions Not all supervised term weighting methods are superior to unsupervised methods (i.e. tf‧x^2, tf‧ig) An adapted learning method is more important than weighting method The best performance of tf‧rf has been analyzed and explained from cross-method comparison, cross-classifier, and cross-corpus validation

  15. Comments • Advantages • The writing structure of this paper is clear • Applications • Text categorization

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