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We propose, in this paper, a possibilistic morphological approach to disambiguate hadiths Arabic texts using semantic knowledge. The disambiguation is considered as a classification problem. The possibilistic approach uses vocalized texts to train a possibilistic classifier in order to classify non-vocalized texts as they are more ambiguous. Morphological attributes are used for training and test. Hadiths are structured in XML format that provides semantic information. We enlarge the classification attributes’ set by adding semantic attributes extracted from the hadiths structure. We prove that the possibilistic approach gives the best rates using AlKhalil analyzer to prepare the training and the test sets. Our proposed possibilistic approach enhances disambiguation rates of Arabic hadiths’ texts when it includes semantic knowledge.

Possibilistic Morphological Disambiguation of Structured Hadiths Arabic Texts using Semantic Knowledge