Term Weighting Approaches for Text Categorization Improving

The objective of the text categorization problem examined in this paper corresponds to automatically distribute the legislative bills to the committees at the Federal District Legislative Assembly in Brasilia, Brazil. For this study the replacement of the idf part in TFIDF by a new term selection measure - absl logit- and by bi-normal separation produced the best general classification results, using support vector machines models (SVM), when compared with TFIDF and with the use of common term selection measures - chi-square, information gain, gain ratio and odds ratio - to replace the idf part in TFIDF.