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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. Outlines. Motivation Objectives Methodology Experiments - PowerPoint PPT Presentation
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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
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
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Outlines
Motivation Objectives Methodology Experiments Conclusions Comments
Intelligent Database Systems Lab
N.Y.U.S.T.
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
Intelligent Database Systems Lab
N.Y.U.S.T.
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
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Methodology
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Text categorization
TF RF‧
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Methodology
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Methodology
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Methodology
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments
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Intelligent Database Systems Lab
N.Y.U.S.T.
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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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
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Comments
· Advantages─ The writing structure of this paper is clear
· Applications─ Text categorization