Arabic morphological analysis and disambiguation using a possibilistic classifier

Authors:
Raja Ayed;Ibrahim Bounhas;Bilel Elayeb;Fabrice Evrard;Narjès Bellamine Ben Saoud
Affiliations:
RIADI Research Laboratory, ENSI Manouba University 2010, Tunisia;Department of Computer Science, Faculty of Sciences of Tunis, University of Tunis, Tunis, Tunisia;RIADI Research Laboratory, ENSI Manouba University 2010, Tunisia,IRIT-ENSEEIHT, Toulouse Cedex 7, France;IRIT-ENSEEIHT, Toulouse Cedex 7, France;RIADI Research Laboratory, ENSI Manouba University 2010, Tunisia
Venue:
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
Year:
2012

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Abstract

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).