Detection and Correction of Non-Words in Arabic: A Hybrid Approach

As Arabic is known for its highly inflectional morphological structure, this hybrid approach is utilizing morphological knowledge in form of consistent root-pattern relationships, and some morpho-syntactical knowledge based on affixation and morphographemic rules to specify the word recognition and non-word correction process. Furthermore this paper is proposing novel probabilistic measures for completing the task of the correction by locating, reducing and ranking of the most probable correction candidates in Arabic derivative words. In this context based on frequency of occurrence analysis, two probabilistic measures are introduced, Root-Pattern Predictive Value, RPV, and Pattern-Root Predictive Value, PPV. Moreover, keyboard effect, letter sound and similarity are considered in addition to some lexical features as a supplementary aid to improve the process of error detection and correction.