Second Language Learners

Shaalan, K., M. Magdy, and A. Fahmy, "Morphological Analysis of Ill-formed Arabic Verbs for Second Language Learners", Applied Natural Language Processing: Identification, Investigation and Resolution, issue Hershey, PA, USA, PA, USA, IGI Global, pp. 1 - 659, 2012. Abstract978-1-60960-741-8.ch022.pdf

Arabic is a language of rich and complex morphology. The nature and peculiarity of Arabic make its morphological and phonological rules confusing for second language learners (SLLs). The conjugation of Arabic verbs is central to the formulation of an Arabic sentence because of its richness of form and meaning. In this research, we address issues related to the morphological analysis of ill-formed Arabic verbs in order to identify the source of errors and provide an informative feedback to SLLs of Arabic. The edit distance and constraint relaxation techniques are used to demonstrate the capability of the proposed system in generating all possible analyses of erroneous Arabic verbs written by SLLs. Filtering mechanisms are applied to exclude the irrelevant constructions and determine the target stem which is used as the base for constructing the feedback to the learner. The proposed system has been developed and effectively evaluated using real test data. It achieved satisfactory results in terms of the recall rate.

Shaalan, K., and M. Magdy, "Adaptive Feedback Message Generation for Second Language Learners of Arabic", Recent Advances in Natural Language Processing (RANLP - 2011),, Hissar, Bulgaria, 12 September , 2011. Abstractr11-1110.pdf

This paper addresses issues related to generating feedback messages to errors related to Arabic verbs made by second language learners (SLLs). The proposed approach allows for individualization. When a SLL of Arabic writes a wrong verb, it performs analysis of the input and distinguishes between different lexical error types. The proposed system issues the intelligent feedback that conforms to the learner’s proficiency level for each class of error. The proposed system has been effectively evaluated using real test data and achieved satisfactory results.

Magdy, M., K. Shaalan, and A. Fahmy, "Lexical Error Diagnosis for Second Language Learners of Arabic", The Seventh Conference on Language Engineering, Egyptian Society of Language Engineering (ELSE), Cairo, Egypt, Dec., 2007. Abstractlexical_error_diagnosis_nle.pdf

This paper addresses the development of an automated lexical error diagnosis system, which helps Arabic second language learners to learn well-formed weak verbs. The learners are encouraged to produce input freely in various situations and contexts and guided to recognize by themselves the erroneous or inappropriate functions of their misused expressions. In this system, we successfully used constraint relaxation and edit-distance techniques to provide error-specific diagnosis and feedback to second language learners of Arabic. We demonstrated the capabilities of these techniques to diagnose errors related to the Arabic weak verb which is formed using complex morphological rules. Furthermore, the developed system allows for individualization of the learning process by providing feedback that conforms to the learner’s expertise. Inexperienced learners might require detailed instruction while experienced learners benefit from higher level reminders and explanations.

Shaalan, K., R. Aref, and A. Fahmy, "An Approach for Analyzing and Correcting Spelling Errors for Non-native Arabic learners", The 7th International Conference on Informatics and Systems (INFOS2010), Cairo, Egypt, Faculty of Comptuers and Information, 2010. Abstractnlp_09_p053-059.pdf

Spell checkers 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.