The Arabic Language Status in the Jordanian Social Networking and Mobile Phone Communications

Abandah, Gheith A.; Khedher, Mohammed Z.; Anati, Waleed A.; Zghoul, Ahmad A.; Ababneh, Sami

doi:10.15849/icit.2015.0083

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…In addition to the above datasets, we test the proposed solutions using samples of real soft spelling mistakes (Test200). These samples were collected in a previous work [8] and are summarized in Table VII. They have a challenging collection of soft spelling mistakes with an average of 6.5 mistakes per sequence.…”

Section: B Data Setsmentioning

confidence: 99%

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Correcting Arabic Soft Spelling Mistakes using BiLSTM-based Machine Learning

Abandah¹,

Suyyagh²,

Khedher³

2022

IJACSA

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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 injection 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

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Section: B Data Setsmentioning

confidence: 99%

“…These dialects are not standardized and vary significantly socially and geographically. Furthermore, they are widely popular in use on social and messaging applications as well on the Internet [8].…”

Section: Introductionmentioning

confidence: 99%