Supervised and Traditional Term Weighting Methods for Automatic Text Categorization

Intelligent Database Systems Lab

國立雲林科技大學National Yunlin University of Science and Technology

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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


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Outlines

Motivation Objectives Methodology Experiments Conclusions Comments


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I. M.Motivation

· The popularly used tf idf‧ method has not shown a uniformly good performance in terms of different data sets

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Text categorization


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I. M.Objectives

· 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?

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Text categorization


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I. M.Methodology

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Text categorization

TF RF‧


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I. M.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

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Comments

· Advantages─ The writing structure of this paper is clear

· Applications─ Text categorization

Supervised and Traditional Term Weighting Methods for Automatic Text Categorization

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