An approach for analyzing and correcting spelling errors for non-native Arabic learners

Spellcheckers are widely used in many software products for identifying errors in users´ writings. However, they are not designed to address spelling errors made by non-native learners of a language. As a matter of fact, spelling errors made by non-native learners are more than just misspellings. Non-native learners´ errors require special handling in terms of detection and correction, especially when it comes to morphologically rich languages such as Arabic, which have few related resources. In this paper, we address common error patterns made by non-native Arabic learners and suggest a two-layer spell-checking approach, including spelling error detection and correction. The proposed error detection mechanism is applied on top of Buckwalter´s Arabic morphological analyzer in order to demonstrate the capability of our approach in detecting possible spelling errors. The correction mechanism adopts a rule-based edit distance algorithm. Rules are designed in accordance with common spelling error patterns made by Arabic learners. Error correction uses a multiple filtering mechanism to propose final corrections. The approach utilizes semantic information given in exercising questions in order to achieve highly accurate detection and correction of spelling errors made by non-native Arabic learners. Finally, the proposed approach was evaluated using real test data and promising results were achieved.