A7׳ta: Data on a monolingual Arabic parallel corpus for grammar checking

Madi, Nora; Al‐Khalifa, Hend S.

doi:10.1016/j.dib.2018.11.146

Cited by 3 publications

(3 citation statements)

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“…As far as we know, we are presenting the first results of applying neural sequence labeling on Arabic text. Additionally, these are the first results of using the A7 ta corpus [33] in an experiment. We believe that the configurations we reported would show good performance on any other comparable Arabic corpus.…”

Section: Conclusion Limitations and Future Workmentioning

confidence: 88%

“…In this section, we present the corpora used for training and testing, the process of preparing them, and their statistics. We first extracted sentences from the "Linguistic Error Detector-Saudi Press as a Sample" book [32] to produce a parallel corpus as described in [33]. It is a monolingual parallel corpus of Arabic text, called A7'ta.…”

Section: Corpus and Annotationmentioning

confidence: 99%

“…All text in the corpora is not diacritized. All datasets have been made public [33] and can be found at https://github.com/iwan-rg/.…”

Section: Corpus and Annotationmentioning

confidence: 99%

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Error Detection for Arabic Text Using Neural Sequence Labeling

Madi

Al‐Khalifa

2020

Applied Sciences

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The English language has, thus far, received the most attention in research concerning automatic grammar error correction and detection. However, these tasks have been less investigated for other languages. In this paper, we present the first experiments using neural network models for the task of error detection for Modern Standard Arabic (MSA) text. We investigate several neural network architectures and report the evaluation results acquired by applying cross-validation on the data. All experiments involve a corpus we created and augmented. The corpus has 494 sentences and 620 sentences after augmentation. Our models achieved a maximum precision of 78.09%, recall of 83.95%, and F0.5 score of 79.62% in the error detection task using SimpleRNN. Using an LSTM, we achieved a maximum precision of 79.21%, recall of 93.8%, and F0.5 score of 79.16%. Finally, the best results were achieved using a BiLSTM with a maximum precision of 80.74%, recall of 85.73%, and F0.5 score of 81.55%. We compared the results of the three models to a baseline, which is a commercially available Arabic grammar checker (Microsoft Word 2007). LSTM, BiLSTM, and SimpleRNN all outperformed the baseline in precision and F0.5. Our work shows preliminary results, demonstrating that neural network architectures for error detection through sequence labeling can successfully be applied to Arabic text.

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Section: Conclusion Limitations and Future Workmentioning

confidence: 88%

Section: Corpus and Annotationmentioning

confidence: 99%