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Combined Optimization of Feature Selection and Algorithm Parameters in Machine Learning of Language

  • Walter Daelemans
  • Véronique Hoste
  • Fien De Meulder
  • Bart Naudts
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2837)

Abstract

Comparative machine learning experiments have become an important methodology in empirical approaches to natural language processing (i) to investigate which machine learning algorithms have the ‘right bias’ to solve specific natural language processing tasks, and (ii) to investigate which sources of information add to accuracy in a learning approach. Using automatic word sense disambiguation as an example task, we show that with the methodology currently used in comparative machine learning experiments, the results may often not be reliable because of the role of and interaction between feature selection and algorithm parameter optimization. We propose genetic algorithms as a practical approach to achieve both higher accuracy within a single approach, and more reliable comparisons.

Keywords

Feature Selection Algorithm Parameter Inductive Logic Programming Word Sense Disambiguation Combine Optimization 
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 2003

Authors and Affiliations

  • Walter Daelemans
    • 1
  • Véronique Hoste
    • 1
  • Fien De Meulder
    • 1
  • Bart Naudts
    • 2
  1. 1.CNTS Language Technology GroupUniversity of AntwerpAntwerpen
  2. 2.Postdoctoral researcher of the Fund for Scientific ResearchISLAB, University of AntwerpFlandersBelgium

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