A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A vector space model for automatic indexing
Communications of the ACM
Concept decompositions for large sparse text data using clustering
Machine Learning
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
A Comparative Study on Feature Selection in 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
A framework of feature selection methods for text categorization
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Expert Systems with Applications: An International Journal
Clustering abstracts of scientific texts using the transition point technique
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
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This paper studies the structure of vectors obtained by using term selection methods in high-dimensional text collection. We found that the distance to transition point (DTP) method omits commonly occurring terms, which are poor discriminators between documents, but which convey important information about a collection. Experimental results obtained on the Reuters-21578 collection with the k-NN classifier show that feature selection by DTP combined with common terms outperforms slightly simple document frequency.