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A Comparative Study on Term Weighting Schemes for Text Classification

  • Ahmad MazyadEmail author
  • Fabien Teytaud
  • Cyril Fonlupt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10710)

Abstract

Text Classification (or Text Categorization) is a popular machine learning task. It consists in assigning categories to documents. In this paper, we are interested in comparing state of the art classifiers and state of the art feature weights. Feature weight methods are classic tools that are used in text categorization. We extend previous studies by evaluating numerous term weighting schemes for state of the art classification methods. We aim at providing a complete survey on text classification for fair benchmark comparisons.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.LISIC, Université du Littoral Côte d’OpaleCalaisFrance

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