Laroussi Merhben, Anis Zouaghi, and Mounir Zrigui
Unité de Recherche en Technologies de l'Information et de la Communication of the Réseau National Universitaire Tunisien, Tunisia (e–mail: aroussi_merhben@hotmail.com; Anis.Zouaghi@gmail.com; mounir.zrigui@fsm.rnu.tn).
Manuscript received June 18, 2012.
Manuscript accepted for publication July 24, 2012.
Abstract
]]> In this paper we test some supervised algorithms that most of the existing related works of word sense disambiguation have cited. Due to the lack of linguistic data for the Arabic language, we work on non–annotated corpus and with the help of four annotators; we were able to annotate the different samples containing the ambiguous words. Since that, we test the Naïve Bayes algorithm, the decision lists and the exemplar based algorithm. During the experimental study, we test the influence of the window size on the disambiguation quality, the derivation and the technique of smoothing for the (2n+1)–grams. For these tests the exemplar based algorithm achieves the best rate of precision.Key words: Supervised algorithms, training data, Naïve Bayes, decision list, exemplar based algorithm, window size.
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