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This paper describes the feature selection method TFIDF (term frequency, inverse document frequency). With it, we process the data resource and set up the vector space model in order to provide a convenient data structure for text categorization. We calculate the precision of this method with the help of categorization results. According to the empirical results, we analyze its advantages and disadvantages and present a new TFIDF-based feature selection approach to improve its accuracy.
Jing et al. (2003) studied this question.