A Comparative Study of Classifier Combination Methods Applied to NLP Tasks

  • Fernando Enríquez
  • José A. Troyano
  • Fermín L. Cruz
  • F. Javier Ortega
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6716)

Abstract

There are many classification tools that can be used for various NLP tasks, although none of them can be considered the best of all since each one has a particular list of virtues and defects. The combination methods can serve both to maximize the strengths of the base classifiers and to reduce errors caused by their defects improving the results in terms of accuracy. Here is a comparative study on the most relevant methods that shows that combination seems to be a robust and reliable way of improving our results.

Keywords

Classifier Combination Machine Learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Fernando Enríquez
    • 1
  • José A. Troyano
    • 1
  • Fermín L. Cruz
    • 1
  • F. Javier Ortega
    • 1
  1. 1.Departamento de Lenguajes y Sistemas InformáticosUniversidad de SevillaSevillaSpain

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