Probabilistic bi-directional root-pattern relationships as cognitive model for semantic processing of arabic

This paper is attempting in terms of a formal statistical language model to review the overall domination of Arabic morphology as a non-linear or non-concatenated processing system in the case of word identification. The basic components of this model are relying on bi-directional probabilistic root-pattern relationships acting as cognitive morphological factors for word recognition. Considering a root in the mental lexicon as the highest level of semantic abstraction for a morpheme allows the view of considering words as a functional or applicative process instantiating the most probable or known pattern to the most plausible root. As Arabic is known for its highly inflectional morphological structure and its high tendency to pattern and root ambiguity (Root-Homonymy and Pattern Polysemy) this model is assuming bi-directional morphological background knowledge for resolving ambiguities in form of probabilistic semantic network. As a major consequence, this paper is stressing the significance of this phenomenon in designing Arabic interactive cognitive systems particularly those related to interactive Arabic natural language understanding and word recognition and corrections.