ICALL

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., "Development of Computer Assisted Language Learning System for Arabic Using Natural Language Processing Techniques", Egyptian Informatics Journal, vol. 4, no. 2: Faculty of Comptuers and Information, pp. 131–155, dec, 2003. Abstractarabic_call_fci.pdf

This paper describes the development of a computer-assisted language learning (CALL) system for learning Arabic using natural language processing (NLP) techniques. This system can be used for learning Arabic by students at the primary schools. It provides grammar practice for learners of Arabic. The learners are encouraged to produce sentences 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 use NLP tools (including a morphological analyzer and syntax analyzer) and an error analyzer to give the adequate feedback to the learner. Furthermore, we propose the mechanism of correction by the learner which allows the learner to correct the typed sentence by herself/himself, and allows the learner to realize that what error she/he has made.

Shaalan, K., "An Intelligent Computer Assisted Language Learning System for Arabic Learners", Computer Assisted Language Learning, vol. 18, no. 1-2: Routledge, part of the Taylor & Francis Group, pp. 81-109, 2005. Abstractarabic_icall.pdfWebsite

This paper describes the development of an intelligent computer-assisted language learning (ICALL) system for learning Arabic. This system could be used for learning Arabic by students at primary schools or by learners of Arabic as a second or foreign language. It explores the use of Natural Language Processing (NLP) techniques for learning Arabic. The learners are encouraged to produce sentences 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 use NLP tools (including morphological analyzer and syntax analyzer) and error analyzer to issue feedback to the learner. Furthermore, we propose a mechanism of correction by the learner which allows the learner to correct the typed sentence independently, and allows the learner to realize that what the error is.

Shaalan, K., and H. Talhami, "Error analysis and handling in Arabic ICALL systems", IASTED International Conference on Artificial Intelligence and Applications (AIA 2006), Innsbruck, Austria, ACTA Press, pp. 109–114, Febrauray, 2006. Abstracterror_analysis_icall.pdf

Arabic is a Semitic language that is rich in its morphology and syntax. The very numerous and complex grammar rules of the language could be confusing even for Arabic native speakers. Many Arabic intelligent computer-assisted language-learning (ICALL) systems have neither deep error analysis nor sophisticated error handling. In this paper, we report an attempt at developing an error analyzer and error handler for Arabic as an important part of the Arabic ICALL system. In this system, the learners are encouraged to construct sentences freely in various contexts and are guided to recognize by themselves the errors or inappropriate usage of their language constructs. We used natural language processing (NLP) tools such as a morphological analyzer and a syntax analyzer for error analysis and to give feedback to the learner. Furthermore, we propose a mechanism of correction by the learner, which allows the learner to correct the typed sentence independently. This will result in the learner being able to figure out what the error is. Examples of error analysis and error handling will be given and will illustrate how the system works.

Shaalan, K. F., M. Magdy, and A. Fahmy, "Morphological Analysis of Ill-Formed Arabic Verbs in Intelligent Language Tutoring Framework", The 23rd International Florida Artificial Intelligence Research Society Conference (FLAIRS-23), Florida, USA, FLAIRS, pp. 277–282, may, 2010. Abstractflairs-23-1755.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 paper, we address issues related to the morphological analysis of ill-formed Arabic verbs in order to identify the source of errors and provide an in-formative feedback to SLLs of Arabic. The edit distance and constraint relaxation techniques are used to demonstrate the capability of the proposed approach 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. A morphological analyzer has been developed and effectively evaluated using real test data. It achieved satisfactory results in terms of the recall rate.

Shaalan, K., M. Magdy, and D. Samy, "Towards Resolving Morphological Ambiguity in Arabic Intelligent Language Tutoring Framework", The seventh international conference on Language Resources and Evaluation (LREC'10) Workshop on Supporting eLearning with Language Resources and Semantic Data, Valletta, Malta, LREC, 2010. Abstractlrec2010elearing_workshop.pdf

Ambiguity is a major issue in any NLP application that occurs when multiple interpretations of the same language phenomenon are produced. Given the complexity of the Arabic morphological system, it is difficult to determine what the intended meaning of the writer is. Moreover, Intelligent Language Tutoring Systems which need to analyze erroneous learner answers, generally, introduce techniques, such as constraints relaxation, that would produce more interpretations than systems designed for processing well-formed input. This paper addresses issues related to the morphological disambiguation of corrected interpretations of erroneous Arabic verbs that were written by beginner to intermediate Second Language Learners. The morphological disambiguation has been developed and effectively evaluated using real test data. It achieved satisfactory results in terms of the recall rate.