A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A patent search and classification system
Proceedings of the fourth ACM conference on Digital libraries
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Choose Your Words Carefully: An Empirical Study of Feature Selection Metrics for Text Classification
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
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We introduce two novel methods of text categorization in which documents are split into fragments. We conducted experiments on English, French and Czech. In all cases, the problems referred to a binary document classification. We find that both methods increase the accuracy of text categorization. For the Naïve Bayes classifier this increase is significant.