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A Semantic Annotation Model for Arabic Legal Texts

This paper addresses an issue in the field of Artificial Intelligence and Law. It's concerned with the problem of automatically identifying and semantically annotating normative provisions' categories in Arabic legal texts. This goal has been achieved through the construction of a semantic annotation model, which combines three linguistic resources namely a taxonomy of Arabic normative provisions' categories, an Arabic normative terminological base and a semantic annotation rule base. The performance of the model has been evaluated over a test dataset of Arabic normative texts collected from the Official Gazette of the Republic of Tunisia. Precision, Recall and F-score measures have been used for evaluation. Obtained results are very promising: 96,4% for Precision, 96,06% for Recall and 96,23% for F-score.