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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Correcting Arabic Soft Spelling Mistakes using BiLSTM-based Machine Learning

Author 1: Gheith Abandah
Author 2: Ashraf Suyyagh
Author 3: Mohammed Z. Khedher

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.0130594

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 5, 2022.

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Abstract: Soft spelling mistakes are a class of mistakes that is widespread among native Arabic speakers and foreign learners alike. Some of these mistakes are typographical in nature. They occur due to orthographic variations of some Arabic letters and the complex rules that dictate their correct usage. Many people forgo these rules, and given the identical phonetic sounds, they often confuse such letters. In this paper, we investigate how to use machine learning to correct such mistakes given that there are no sufficient datasets to train the correction models. Soft errors detection and correction is an active field in Arabic natural language processing. We generate training datasets using proposed transformed input approach and stochastic error injec-tion approach. These approaches are applied to two acclaimed datasets that represent Classical Arabic and Modern Standard Arabic. We treat the problem as character-level, one-to-one sequence transcription problem. This one-to-one transcription of mistakes that include omissions and deletions is possible with adopted simple transformations. This approach permits using bidirectional long short-term memory (BiLSTM) models that are more effective to train compared to other alternatives such as encoder-decoder models. Based on investigating multiple alternatives, we recommend a configuration that has two BiLSTM layers, and is trained using the stochastic error injection approach with error injection rate of 40%. The best model corrects 96.4%of the injected errors and achieves a low character error rate of 1.28% on a real test set of soft spelling mistakes.

Keywords: Arabic text; natural language processing; spelling mistakes; recurrent neural networks; bidirectional long short-term memory

Gheith Abandah, Ashraf Suyyagh and Mohammed Z. Khedher, “Correcting Arabic Soft Spelling Mistakes using BiLSTM-based Machine Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 13(5), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130594

@article{Abandah2022,
title = {Correcting Arabic Soft Spelling Mistakes using BiLSTM-based Machine Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130594},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130594},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {5},
author = {Gheith Abandah and Ashraf Suyyagh and Mohammed Z. Khedher}
}


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