Natural language information retrieval: progress report
Information Processing and Management: an International Journal - The sixth text REtrieval conference (TREC-6)
A vector space model for automatic indexing
Communications of the ACM
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Modern Information Retrieval
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Use of a Weighted Topic Hierarchy for Document Classification
TSD '99 Proceedings of the Second International Workshop on Text, Speech and Dialogue
BUAP-UPV TPIRS: a system for document indexing reduction at WebCLEF
CLEF'05 Proceedings of the 6th international conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories
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
Expert Systems with Applications: An International Journal
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Term selection process is a very necessary component for most natural language processing tasks. Although different unsupervised techniques have been proposed, the best results are obtained with a high computational cost, for instance, those based on the use of entropy. The aim of this paper is to propose an unsupervised term selection technique based on the use of a bigram-enriched version of the transition point. Our approach reduces the corpus vocabulary size by using the transition point technique and, thereafter, it expands the reduced corpus with bigrams obtained from the same corpus, i.e., without external knowledge sources. This approach provides a considerable dimensionality reduction of the TREC-5 collection and, also has shown to improve precision for some entropy-based methods.