spell checking

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Shaalan, K., Y. Samih, M. Attia, P. Pecina, and J. van Genabith, "Arabic Word Generation and Modelling for Spell Checking", The eighth international conference on Language Resources and Evaluation (LREC'12), Istanbul, Turkey, 24 May , 2012. Abstract603_paper.pdf

Arabic is a language known for its rich and complex morphology. Although many research projects have focused on the problem of Arabic morphological analysis using different techniques and approaches, very few have addressed the issue of generation of fully inflected words for the purpose of text authoring.
Available open-source spell checking resources for Arabic are too small and inadequate. Ayaspell, for example, the official resource used with OpenOffice applications, contains only 300,000 fully inflected words. We try to bridge this critical gap by creating an adequate, open-source and large-coverage word list for Arabic containing 9,000,000 fully inflected surface words. Furthermore, from a large list of valid forms and invalid forms we create a character-based tri-gram language model to approximate knowledge about permissible character clusters in Arabic, creating a novel method for detecting spelling errors. Testing of his language model gives a precision of 98.2% at a recall of 100%. We take our research a step further by creating a context-independent spelling correction tool using a finite-state automaton that measures the edit distance between input words and candidate corrections, the Noisy Channel Model, and knowledge-based rules. Our system performs significantly better than Hunspell in choosing the best solution, but it is still below the MS Spell Checker.

Attia, M., P. Pecina, Y. Samih, K. Shaalan, and J. van Genabith, "Improved Spelling Error Detection and Correction for Arabic", The International Conference on Computational Linguistics (COLING), Mumbai, India, 14 December, 2012. Abstractimproved_spelling.pdf

A spelling error detection and correction application is based on three main components: a dictionary (or reference word list), an error model and a language model. While most of the attention in the literature has been directed to the language model, we show how improvements in any of the three components can lead to significant cumulative improvements in the overall performance of the system. We semi-automatically develop a dictionary of 9.3 million fully inflected Arabic words using a morphological transducer and a large corpus. We improve the error model by analysing error types and creating an edit distance based re-ranker. We also improve the language model by analysing the level of noise in different sources of data and selecting the optimal subset to train the system on. Testing and evaluation experiments show that our system significantly outperforms Microsoft Word 2010, OpenOffice Ayaspell and Google Docs.

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.

Shaalan, K., A. Allam, and A. Gomah, "Towards automatic spell checking for Arabic", Proceedings of the Fourth Conference on Language Engineering, Egyptian Society of Language Engineering (ELSE), Egypt: Faculty of Engineering, pp. 240–247, oct, 2003. Abstractspellcheck.pdf

Arabic's rich morphology (word construction) and complex orthography (writing system) present unique challenges for automatic spell checking. An Arabic checker attempts to find a dictionary word that might be the correct spelling of the misspelled or misrecognized word. In this paper, we report our attempt in developing an Arabic spelling checker program for solving this problem. Our approach is heuristic and involves developing an Arabic morphological analyzer, techniques of spelling checking and spelling correction, and efficient methods of lexicon operations. The developed Arabic spell checker is able to recognize common spelling errors for standard Arabic and Egyptian dialects.