Arabic Morphological Analysis and Disambiguation Using a Possibilistic Classifier

  • Raja Ayed
  • Ibrahim Bounhas
  • Bilel Elayeb
  • Fabrice Evrard
  • Narjès Bellamine Ben Saoud
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7390)


This paper proposes and experiments a new approach for morphological feature disambiguation of non-vocalized Arabic texts using a possibilistic classifier. The main idea is to learn contextual dependencies between features from vocalized texts and exploit this knowledge to disambiguate non-vocalized ones. We use possibility theory as a means to model imprecision in the training and testing steps, since the context is itself ambiguous. We also investigate the dependency between various features focusing on the Part-Of-Speech (POS).


Morphological Analysis Morphological Disambiguation Possibilistic Classification Morphological features 


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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Raja Ayed
    • 1
  • Ibrahim Bounhas
    • 3
  • Bilel Elayeb
    • 1
    • 2
  • Fabrice Evrard
    • 2
  • Narjès Bellamine Ben Saoud
    • 1
  1. 1.RIADI Research LaboratoryENSI Manouba University 2010Tunisia
  2. 2.IRIT-ENSEEIHTToulouse Cedex 7France
  3. 3.Department of Computer Science, Faculty of Sciences of TunisUniversity of TunisTunisTunisia

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