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Specificity Helps Text Classification

  • Lucas Bouma
  • Maarten de Rijke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3936)

Abstract

We examine the impact on classification effectiveness of semantic differences in categories. Specifically, we measure broadness and narrowness of categories in terms of their distance to the root of a hierarchically organized thesaurus. Using categories of four different levels degrees of broadness, we show that classifying documents into narrow categories gives better scores than classifying them into broad terms, which we attribute to the fact that more specific categories are associated with terms with a higher discriminatory power.

Keywords

Kainic Acid Category Label Trypanosoma Cruzi Entamoeba Histolytica Trypanosoma Brucei 
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 2006

Authors and Affiliations

  • Lucas Bouma
    • 1
  • Maarten de Rijke
    • 1
  1. 1.ISLAUniversity of AmsterdamAmsterdamThe Netherlands

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