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http://dx.doi.org/10.5762/KAIS.2010.11.12.5089

A Research on Enhancement of Text Categorization Performance by using Okapi BM25 Word Weight Method  

Lee, Yong-Hun (Dept. of Computer science, Dankook University)
Lee, Sang-Bum (Dept. of Computer science, Dankook University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.11, no.12, 2010 , pp. 5089-5096 More about this Journal
Abstract
Text categorization is one of important features in information searching system which classifies documents according to some criteria. The general method of categorization performs the classification of the target documents by eliciting important index words and providing the weight on them. Therefore, the effectiveness of algorithm is so important since performance and correctness of text categorization totally depends on such algorithm. In this paper, an enhanced method for text categorization by improving word weighting technique is introduced. A method called Okapi BM25 has been proved its effectiveness from some information retrieval engines. We applied Okapi BM25 and showed its good performance in the categorization. Various other words weights methods are compared: TF-IDF, TF-ICF and TF-ISF. The target documents used for this experiment is Reuter-21578, and SVM and KNN algorithms are used. Finally, modified Okapi BM25 shows the most excellent performance.
Keywords
Text Categorization; Document Classification; TF-IDF; TF-ICF; TF-ISF; Okapi BM25; SVM; Reuter-21578;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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