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GenDesc: A Partial Generalization of Linguistic Features for Text Classification

  • Guillaume Tisserant
  • Violaine Prince
  • Mathieu Roche
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7934)

Abstract

This paper presents an application that belongs to automatic classification of textual data by supervised learning algorithms. The aim is to study how a better textual data representation can improve the quality of classification. Considering that a word meaning depends on its context, we propose to use features that give important information about word contexts. We present a method named GenDesc, which generalizes (with POS tags) the least relevant words for the classification task.

Keywords

Ranking Function Textual Data Sentiment Analysis Linguistic Feature Inverse Document Frequency 
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 2013

Authors and Affiliations

  • Guillaume Tisserant
    • 1
  • Violaine Prince
    • 1
  • Mathieu Roche
    • 1
  1. 1.LIRMM, CNRSUniv Montpellier 2MontpellierFrance

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