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A Competitive Term Selection Method for Information Retrieval

  • Franco Rojas López
  • Héctor Jiménez-Salazar
  • David Pinto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4394)

Abstract

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.

Keywords

Information Retrieval Term Selection Vector Space Model Important Term Vocabulary Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Franco Rojas López
    • 1
  • Héctor Jiménez-Salazar
    • 1
  • David Pinto
    • 1
    • 2
  1. 1.Faculty of Computer Science, BUAP, Puebla, 72570 Ciudad UniversitariaMexico
  2. 2.Department of Information Systems and Computation, UPV, Valencia 46022, Camino de Vera s/nSpain

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