Contribution to semantic analysis of Arabic language
Advances in Artificial Intelligence
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In this paper we put forward an unsupervised system WSD-AL for Arabic word disambiguation. We apply some pre-processing steps to texts containing the ambiguous word in the corpus and we extract the most relevant words. Then, we put to use the Context-Matching algorithm that returns a semantic coherence score corresponding to the context of use that is semantically closest to the original sentence. These Contexts are generated using the glosses of the ambiguous word and the corpus. The results found by the proposed system are satisfactory, as the rate of disambiguation obtained equals 78.