Experimenting a discriminative possibilistic classifier with reweighting model for Arabic morphological disambiguation. (September 2015)

• Record Type: Journal Article
• Authors: Bounhas, Ibrahim; Ayed, Raja; Elayeb, Bilel; Evrard, Fabrice; Bellamine Ben Saoud, Narjès
• Is Part Of: Computer speech & language. Volume 33(2015)
• Page Start: 67
• Page End: 87
• Publication Date: 2015-09
• Subjects: Morphological analysis -- Morphological disambiguation -- Discriminative possibilistic classifier -- Reweighting model; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2014.12.005
• Languages: English
• ISSNs: 0885-2308

Abstract

Highlights: We perform Arabic morphological disambiguation on unlabeled vocalized corpora. We experiment possibilistic measures for imprecise morphological data classification. We assess the impact of a reweighting model and a possibilistic lexical likelihood. Possibilistic classification is accurate in modern and classical texts disambiguation. Abstract: In this paper, we experiment a discriminative possibilistic classifier with a reweighting model for morphological disambiguation of Arabic texts. The main idea is to provide a possibilistic classifier that acquires automatically disambiguation knowledge from vocalized corpora and tests on non-vocalized texts. Initially, we determine all the possible analyses of vocalized words using a morphological analyzer. The values of their morphological features are exploited to train the classifier. The testing phase consists in identifying the accurate class value (i.e., a morphological feature) using the features of the preceding and the following words. The appropriate class is the one having the greatest value of a possibilistic measure computed over the training set. To discriminate the effect of each feature, we add the weights of the training attributes to this measure. To assess this approach, we carry out experiments on a corpus of Arabic stories and on the Arabic Treebank. We present results concerning all the morphological features and we discern to which degree the discriminative approach improves disambiguation rates and extract the dependency relationships among the features. The results reveal the contribution of possibility theory for resolving ambiguities in real applications. We also compare the success rates in modern versus classical Arabic texts. Finally, we try to evaluate the impact of the lexical likelihood in morphological disambiguation.