A Comparison of Word- and Sense-Based Text Categorization Using Several Classification Algorithms

  • Authors:

  • Athanasios Kehagias;Vassilios Petridis;Vassilis G. Kaburlasos;Pavlina Fragkou

  • Affiliations:

  • Aristotle University of Thessaloniki (AUTh), Department of Math., Phys. and Comp. Sciences, Division of Mathematics, GR-54124 Thessaloniki, Greece;Aristotle University of Thessaloniki (AUTh), Department of Electrical and Computer Engineering, Division of Electronics and Computer Engineering, GR-54124 Thessaloniki, Greece;Technological Educational Institute of Kavala, Department of Industrial Informatics, Division of Software Systems, GR-65404 Kavala, Greece;Aristotle University of Thessaloniki (AUTh), Department of Electrical and Computer Engineering, Division of Electronics and Computer Engineering, GR-54124 Thessaloniki, Greece

  • Venue:
  • Journal of Intelligent Information Systems
  • Year:
  • 2003

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Abstract

Most of the text categorization algorithms in the literature represent documents as collections of words. An alternative which has not been sufficiently explored is the use of word meanings, also known as senses. In this paper, using several algorithms, we compare the categorization accuracy of classifiers based on words to that of classifiers based on senses. The document collection on which this comparison takes place is a subset of the annotated Brown Corpus semantic concordance. A series of experiments indicates that the use of senses does not result in any significant categorization improvement.