Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
The nature of statistical learning theory
The nature of statistical learning theory
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
The feature quantity: an information theoretic perspective of Tfidf-like measures
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Modern Information Retrieval
High-performing feature selection for text classification
Proceedings of the eleventh international conference on Information and knowledge management
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Experiments on the Use of Feature Selection and Negative Evidence in Automated Text Categorization
ECDL '00 Proceedings of the 4th European Conference on Research and Advanced Technology for Digital Libraries
An analysis of the relative hardness of Reuters-21578 subsets: Research Articles
Journal of the American Society for Information Science and Technology
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
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Traditional tfidf-like term weighting schemes have a rough statistic -- idfas the term weighting factor, which does not exploit the category information (category labels on documents) and intra-document information (the relative importance of a given term to a given document that contains it) from the training data for a text categorization task. We present here a more elaborate nonparametric probabilistic model to make use of this sort of information in the term weighting phase. idfis theoretically proved to be a rough approximation of this new term weighting factor. This work is preliminary and mainly aiming at providing inspiration for further study on exploitation of this information, but it already provides a moderate performance boost on three popular document collections.