Authors:
Bilel Elayeb
1
;
2
;
Mohamed Firas Ettih
3
and
Raja Ayed
4
;
2
Affiliations:
1
Liwa College of Technology, P.O. Box 41009, Abu Dhabi, U.A.E.
;
2
RIADI Research Laboratory, ENSI, Manouba University, Tunisia
;
3
Université Paris-Est Créteil, Paris 12 Val de Marne, France
;
4
Faculty of Economics and Management of Nabeul, Carthage University, Tunisia
Keyword(s):
Morphological Disambiguation, Arabic Text, Machine-Learning Algorithms, Data Transformation, Morphological Feature, Classification.
Abstract:
Arabic language is characterized by its complexity and its morphological and orthographic variations including syntactic and semantic diversity of a word. This specificity may cause Arabic morphological ambiguity. We present in this paper a new architecture for morphological disambiguation of Arabic texts. The latter can be treated as a classification problem where the set of morphological features’ values represent classes, and a classification algorithm is used to assign a class to each word’s occurrence based on the context. The first step consists of identifying the correct morphological analysis of a non-vocalized Arabic word using the morphological dependencies extracted from the corpus of vocalized texts. Then, we propose a method of transforming imperfect training datasets into perfect data having precise attributes and certain classes. We experiment this architecture on a set of machine-learning classifiers using a corpus of classic Arabic texts. Results highlight some stati
stically significant improvement of SVM and Naïve Bayes classifiers in terms of disambiguation rate.
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