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Analysis and feedback of erroneous Arabic verbs

Published online by Cambridge University Press:  16 September 2013

KHALED SHAALAN ,
MARWA MAGDY  and
ALY FAHMY
KHALED SHAALAN
Affiliation:
School of Informatics, University of Edinburgh, Edinburgh, UK; The British University in Dubai, P.O. Box 345015, Dubai, UAE e-mail: {Khaled.shaalan@buid.ac.ae}
MARWA MAGDY
Affiliation:
IBM Egypt – Cairo Technology Development Centre, Pyramids Heights Office Park, Building C10, Cairo–Alexandria Desert Road, KM 22, P.O. Box 166, El-Ahram, Giza, Egypt e-mail: {marwam@eg.ibm.com}
ALY FAHMY
Affiliation:
Faculty of Computers & Information, Cairo University, 5 Ahmed Zewel St., Giza 12613, Egypt e-mail: {a.fahmy@fci-cu.edu.eg}
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Abstract

Arabic language is strongly structured and considered as one of the most highly inflected and derivational languages. Learning Arabic morphology is a basic step for language learners to develop language skills such as listening, speaking, reading, and writing. Arabic morphology is non-concatenative and provides the ability to attach a large number of affixes to each root or stem that makes combinatorial increment of possible inflected words. As such, Arabic lexical (morphological and phonological) rules may be confusing for second language learners. Our study indicates that research and development endeavors on spelling, and checking of grammatical errors does not provide adequate interpretations to second language learners’ errors. In this paper we address issues related to error diagnosis and feedback for second language learners of Arabic verbs and how they impact the development of a web-based intelligent language tutoring system. The major aim is to develop an Arabic intelligent language tutoring system that solves these issues and helps second language learners to improve their linguistic knowledge. Learners are encouraged to produce input freely in various situations and contexts, and are guided to recognize by themselves the erroneous functions of their misused expressions. Moreover, we proposed a framework that allows for the individualization of the learning process and provides the intelligent feedback that conforms to the learner's expertise for each class of error. Error diagnosis is not possible with current Arabic morphological analyzers. So constraint relaxation and edit distance techniques are successfully employed to provide error-specific diagnosis and adaptive feedback to learners. We demonstrated the capabilities of these techniques in diagnosing errors related to Arabic weak verbs formed using complex morphological rules. As a proof of concept, we have implemented the components that diagnose learner's errors and generate feedback which have been effectively evaluated against test data acquired from real teaching environment. The experimental results were satisfactory, and the performance achieved was 74.34 percent in terms of recall rate.

Type
Articles
Information
Natural Language Engineering , Volume 21 , Issue 2 , March 2015 , pp. 271 - 323
Copyright
Copyright © Cambridge University Press 2013 

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References

Abdel Monem, A., Shaalan, K., Rafea, A., and Baraka, H. 2008. Generating Arabic text in multilingual speech-to-speech machine translation framework. Machine Translation 20 (4): 205–58 (Springer, Netherlands).Google Scholar
Alfaifi, A., and Atwell, E. 2012. Arabic Learner Corpora (ALC): a taxonomy of coding errors. In Proceedings of the 8th International Computing Conference in Arabic (ICCA 2012), Cairo, Egypt.Google Scholar
Ali, A. 2013, January. The verbal system of Malay and Arabic. Contrastive Analysis 4 (1): 21–37 (Centre for Promoting Ideas (CPI), USA).Google Scholar
Al-Jumaily, H., Martínez, P., Martínez-Fernández, J., and Van der Goot, E. 2011). A real time named entity recognition system for Arabic text mining. Language Resources & Evaluation Journal, doi:10.1007/s10579-011-9146-z.Google Scholar
Attia, M., Pecina, P., Samih, Y., Shaalan, K., and Genabith, J. 2012. Improved spelling error detection and correction for Arabic. In The International Conference on Computational Linguistics (COLING), Mumbai, India, 8–15 December 2012, pp. 103–12.Google Scholar
Bigert, J. 2004. Probabilistic detection of context sensitive spelling errors. In Proceedings of 4th International Conference on Language Resources and Evaluation (LREC 04), Lissabon, Portugal, pp. 1633–6.Google Scholar
Bowden, T., and Kiraz, G. A. 1995. A morphographemic model for error correction in non-concatenative strings. In Proceedings of 33rd Annual Meeting of the Association for Computational Linguistics (ACL-95), Boston, MA, pp. 24–30.CrossRefGoogle Scholar
Buckwalter, T., 2002. Buckwalter Arabic Morphological Analyzer Version 1.0. Linguistic Data Consortium. University of Pennsylvania, LDC Catalog No.: LDC2002L49, ISBN: 1-58563-257-0.Google Scholar
Cavalli-Sforza, V., Soudi, A., and Mitamura, T. 2000. Arabic morphology generation using a concatenative strategy. In Proceedings of the 1st Conference, North American Chapter of the Association for Computational Linguistics (NAACL 00), Seattle, WA, pp. 86–93.Google Scholar
Daciuk, J. 1998. Incremental Construction of Finite-State Automata and Transducers, and their Use in Natural Language Processing. PhD thesis, Technical University of Gdansk, Poland.CrossRefGoogle Scholar
Elmi, M. A., and Evens, M. 1998. Spelling correction using context. In Proceedings of 36th Annual Meeting of the Association for Computational Linguistics (ACL 98), Montreal, Canada, pp. 360–64.Google Scholar
El-Sadany, T. A., and Hashish, M. A., 1989. An Arabic morphological system. IBM Systems Journal 28 (4): 600–12.CrossRefGoogle Scholar
Faltin, A. V. 2003. Syntactic Error Diagnosis in the Context of Computer-Assisted Language Learning. PhD thesis, University of Geneva, Switzerland.Google Scholar
Faltin, A. V., L’haire, S., and Ndiaye, M. 2005. A spell-checker for language learners of French and a learner corpus. In Proceedings of European Association for Computer-Assisted Language Learning (EUROCALL 05), Cracow, Poland.Google Scholar
Farghaly, A., and Shaalan, K. 2009. Arabic natural language processing: challenges and solutions. ACM Transactions on Asian Language Information Processing (TALIP; the Association for Computing Machinery (ACM)) 8 (4): 1–22.CrossRefGoogle Scholar
Habash, N. (2010). Introduction to Arabic natural language processing. Synthesis Lectures on Human Language Technologies 3 (1): 1–187 (Morgan & Claypool Publishers).Google Scholar
Habash, N., and Roth, R., 2011. Using deep morphology to improve automatic error detection in Arabic handwriting recognition. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, Portland, OR, 19–24 June, pp. 875–84.Google Scholar
Heift, T. 1998a. Designed Intelligence: A Language Teacher Model. PhD thesis, Simon Fraser University, Canada.Google Scholar
Heift, T. 1998b. An interactive intelligent language tutor over the Internet. In Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications (ED-MEDIA 1998), Charlottesville, VA (AACE), pp. 508–12.Google Scholar
Heift, T., Toole, J., McFetridge, P., Popowich, F., and Tsiplakou, S., 2000. Learning Greek with an intelligent and adaptive hypermedia system. IME Journal of Computer-Enhanced Learning 2 (2): 58–79.Google Scholar
Hsieh, C.-C., Tsai, T.-H., Wible, D., and Hsu, W.-L. 2002. Exploiting knowledge representation in an intelligent tutoring system for English lexical errors. In Proceedings of the International Conference on Computers in Education (ICCE 2002), Auckland, New Zealand, pp. 115–6.CrossRefGoogle Scholar
Ingels, P. 1997. A Robust Text Processing Technique Applied to Lexical Error Recovery. PhD thesis, Linkping University, Sweden.Google Scholar
Jassem, J. A., 2000. Study on Second Language Learners of Arabic: An Error Analysis Approach. Kuala Lumpur, Malaysia: A.S. Noordeen. ISBN: 983-065-093-6.Google Scholar
Magdy, M., Shaalan, K., and Fahmy, A. 2007. Lexical error diagnosis for second language learners of Arabic. In Proceedings of the Seventh Conference on Language Engineering, Egyptian Society of Language Engineering (ELSE), Cairo, Egypt, 5–6 December, pp. 86–101.Google Scholar
Rimrott, A. 2003. SANTY: A spell-checking algorithm for treating predictable verb inflection mistakes made by non-native writers of German. Term Paper for LING 807, Computational Linguistics, Simon Fraser University, Burnaby, Canada.Google Scholar
Rimrott, A. 2005a. Spell Checking in Computer-Assisted Language Learning: A Study of Misspellings by Non-native Writers of German. Master's thesis, University of Simon Fraser, Canada.Google Scholar
Ryding, K., 2005b. Reference Grammar of Modern Standard Arabic. New York, NY: Cambridge University Press.Google Scholar
Shaalan, K. 2005a, February. An intelligent computer-assisted language learning system for Arabic learners. Computer-Assisted Language Learning 18 (1&2), 81–108 (Taylor & Francis).CrossRefGoogle Scholar
Shaalan, K. 2005b, June. Arabic GramCheck: a grammar checker for Arabic. Software Practice and Experience 35 (7), 643–65 (John Wiley, UK).Google Scholar
Shaalan, K. 2010, June. Rule-based Approach in Arabic Natural Language Processing, Special Issue on Advances in Arabic Language Processing, the International Journal on Information and Communication Technologies (IJICT), Serial Publications, New Delhi, India, 3 (3):11–19.Google Scholar
Shaalan, K., Allam, A., and Gomah, A. 2003. Towards automatic spell-checking for Arabic. In Proceedings of the 4th Conference on Language Engineering, Egyptian Society of Language Engineering (ELSE), Cairo, Egypt, 21–22 October 2003, pp. 240–7.Google Scholar
Shaalan, K., Aref, R., and Fahmy, A. 2010c. An approach for analyzing and correcting spelling errors for non-native Arabic learners. In Proceedings of The 7th International Conference on Informatics and Systems (INFOS2010), Special Track on Natural Language Processing and Knowledge Mining, Cairo, Egypt, 28–30 March, pp. 53–59.Google Scholar
Shaalan, K., and Attia, M. 2012. Handling unknown words in Arabic FST morphology. In International Workshop on Finite State Methods and Natural Language Processing (FSMNLP 2012), 10th edition, Donostia, San Sebastian, Spain, 23–25 July, pp. 20–24.Google Scholar
Shaalan, K., and Magdy, M. 2011. Adaptive feedback message generation for second language learners of Arabic. International Conference on Recent Advances in Natural Language Processing (RANLP-2011), Augusta, Hissar, Bulgaria, 12–14 September, pp. 752–7.Google Scholar
Shaalan, K., Magdy, M., and Fahmy, A. 2010a. Morphological analysis of ill-formed Arabic verbs in intelligent language tutoring framework. In Proceedings of the 23rd International Florida Artificial Intelligence Research Society Conference (FLAIRS-23), Applied Natural Language Processing Track, Palm Beach FL, 19–21 May, pp. 277–82.Google Scholar
Shaalan, K., Magdy, M., and Fahmy, A. 2012. Morphological analysis of il-formed Arabic verbs for second language learners, Chapter 22. In McCarthy, P. and Boonthum, C. (eds.), Applied Natural Language Processing: Identification, Investigation and Resolution, pp. 383–97. Hershey, PA: IGI Global.Google Scholar
Shaalan, K., Magdy, M., and Samy, D. 2010b. Towards resolving morphological ambiguity in Arabic intelligent language tutoring framework. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10) Workshop on Supporting eLearning with Language Resources and Semantic Data, Valletta, Malta, 22 May, pp. 12–17.Google Scholar
Soudi, A., Cavalli-Sforza, V., and Jamari, A. 2001. A computational lexeme-based treatment of Arabic morphology. In Proceedings of the Workshop on Arabic Language Processing: Status and Prospects (ACL 01), Toulouse, France, pp. 155–62.Google Scholar
Tschichold, C. 2003. Lexically driven error detection and correction. CALICO Journal 20 (3), 549–59.Google Scholar