The First Persian Context Sensitive Spell Checker

In this article an attempt to introduce the first Persian context sensitive spell checker, which tries to detect and correct the :eat-word spelling error of Persian text is presented. The proposed method is a statistical approach which uses Bayesian framework as its probabilistic model and also uses mutual information metric as a semantic relatedness measure between different Persian words. Our experiments on sample test data, shows that accuracy of correction method is about ۸۰% with respect to Fl-measure.

Keywords:

Persian language , real-word error , Bayesian framework , mutual information