Improving Arabic Texts Morphological Disambiguation Using a Possibilistic Classifier

A hybrid possibilistic approach for Arabic full morphological disambiguation

“…Indeed, this paper is an extended version of our work in [27]. This new contribution stands out by the following aspects.…”

“…Thus, we run 10 iterations where 90% of the words are used for training and 10% for testing. To get the disambiguation rates of each feature, we perform as follows: (a) we analyze the vocalized texts and we save the correct morphological solutions; (b) we remove the short vowels of the same texts; (c) we disambiguate the obtained texts with our classifier and we store the results; finally (d) we compare the two results [27]. The morphological feature of a non-vocalized word is correct when its classification result corresponds to the result given to its associated vocalized word.…”

“…We obtain different classifiers combining possibility, necessity and entropy. Second, our work in [27] focused mainly on a statistical approach, while we integrate in this new contribution linguistic rules (cf. Section 3.4) and study their effect on the disambiguation rates (cf.…”

Combining Indexing Units for Arabic Information Retrieval

“…Stem indexes reduce ambiguity (Ayed, 2014;Bounhas et al, 2015), but it will from the other side, reduce recall, since some studies (Al-Kabi et al, 2011) showed that in most cases, morphological variants of words have somehow similar semantic interpretations and are not completely dissimilar and different word forms may bear similar meaning. When we apply a light stemming to the same example, "alinkissamat-‫"االنقسامات‬ (the divisions) and "inkissam-‫"انقسام‬ (division) will have the same stem but their semantic relation with "kisma ‫"قسمة-‬ (division) will be ignored.…”

Arabic Text Mining a Systematic Review of the Published Literature 2002-2014

“…Classification [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33] , [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [18], [48], [49], [50] 31(29%) Stemming and Lemmatization [51], [52], [53], [54], [55], [4], [56], [57], [58], [59], [60], [61] 12(11%) Information Retrieval [9], [62], [63], [64], [65], [66], [67], …”