Improving Arabic Texts Morphological Disambiguation Using a Possibilistic Classifier

Morphological ambiguity is an important problem that has been studied through different approaches. We investigate, in this paper, some classification methods to disambiguate Arabic morphological features of non-vocalized texts. A possibilistic approach is improved and proposed to handle imperfect training and test datasets. We introduce a data transformation method to convert the imperfect dataset to a perfect one. We compare the disambiguation results of classification approaches to results given by the possibilistic classifier dealing with imperfection context.

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