Open Access
Issue |
ITM Web Conf.
Volume 56, 2023
First International Conference on Data Science and Advanced Computing (ICDSAC 2023)
|
|
---|---|---|
Article Number | 04009 | |
Number of page(s) | 14 | |
Section | Language & Image Processing | |
DOI | https://doi.org/10.1051/itmconf/20235604009 | |
Published online | 09 August 2023 |
- Al-Qaraghuli, M. (2021). Correcting Arabic Soft Spelling Mistakes Using Transformers. 146-151. [Google Scholar]
- Antoun, W., Baly, F., & Hajj, H. (2020). AraBERT: Transformer-based Model for Arabic Language Understanding. ArXiv, May, 9-15. [Google Scholar]
- Chouikhi, H., & Alsuhaibani, M. (2022). Deep Transformer Language Models for Arabic Text Summarization: A Comparison Study. Applied Sciences (Switzerland), 12(23). https://doi.org/10.3390/app122311944 [Google Scholar]
- Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. NAACL HLT 2019-2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, 1(Mlm), 4171-4186. [Google Scholar]
- Ethnologue. Arabic language statistics, 2020. [Google Scholar]
- Karita, S., Wang, X., Watanabe, S., Yoshimura, T., Zhang, W., Chen, N., Hayashi, T., Hori, T., Inaguma, H., Jiang, Z., Someki, M., Soplin, N. E. Y., & Yamamoto, R. (2019). A Comparative Study on Transformer vs RNN in Speech Applications. 2019 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2019 - Proceedings, 9(4), 449-456. https://doi.org/10.1109/ASRU46091.2019.9003750 [Google Scholar]
- Madi, N., & Al-Khalifa, H. (2020). Error detection for Arabic text using neural sequence labeling. Applied Sciences (Switzerland), 10(15), 1-14. https://doi.org/10.3390/APP10155279 [Google Scholar]
- Madi, N., & Al-Khalifa, H. S. (2018). A Proposed Arabic Grammatical Error Detection Tool Based on Deep Learning. Procedía Computer Science, 142, 352-355. https://doi.org/10.1016/j.procs.2018.10.482 [CrossRef] [Google Scholar]
- Montejo-Ráez, A., & Jiménez-Zafra, S. M. (2022). Current Approaches and Applications in Natural Language Processing. Applied Sciences (Switzerland), 12(10), 10-15. https://doi.org/10.3390/app12104859 [Google Scholar]
- Parnow, K., Li, Z., & Zhao, H. (2020). Grammatical Error Correction: More Data with More Context. 24-29. https://doi.org/10.1109/IALP51396.2020.9310498 [Google Scholar]
- Pires, T., Schlinger, E., & Garrette, D. (2020). How multilingual is multilingual BERT? ACL 2019-57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, 4996-5001. https://doi.org/10.18653/v1/p19-1493 [Google Scholar]
- Qiu, Z., & Qu, Y. (2019). A Two-Stage Model for Chinese Grammatical Error Correction. IEEE Access, 7, 146772-146777. https://doi.org/10.1109/ACCESS.2019.2940607 [CrossRef] [Google Scholar]
- Sarhan, I., & Spruit, M. (2020). Can we survive without labelled data in NLP? Transfer learning for open information extraction. Applied Sciences (Switzerland), 10(17). https://doi.org/10.3390/APP10175758 [Google Scholar]
- Shaalan, K. F. (2005). Arabic GramCheck: a grammar checker for Arabic. September 2004, 643-665. https://doi.org/10.1002/spe.653 [Google Scholar]
- Singh, S., & Mahmood, A. (2021). The NLP Cookbook: Modern Recipes for Transformer Based Deep Learning Architectures. 68675-68702. https://doi.org/10.1109/ACCESS.2021.3077350 [Google Scholar]
- Solyman, A., Wang, Z., & Tao, Q. (2019). Proposed model for arabic grammar error correction based on convolutional neural network. Proceedings of the International Conference on Computer, Control, Electrical, and Electronics Engineering 2019, ICCCEEE 2019. https://doi.org/10.1109/ICCCEEE46830.2019.9071310 [Google Scholar]
- Solyman, A., Zhenyu, W., Qian, T., Abdulgader, A., Elhag, M., & Toseef, M. (2021). Synthetic data with neural machine translation for automatic correction in arabic grammar. Egyptian Informatics Journal, 22(3), 303-315. https://doi.org/10.1016/j.eij.2020.12.001 [CrossRef] [Google Scholar]
- Zaghouani, W., Mohit, B., Habash, N., Obeid, O., Tomeh, N., Rozovskaya, A., Farra, N., Alkuhlani, S., & Oflazer, K. (2014). Large scale Arabic error annotation: Guidelines and framework. Proceedings of the 9th International Conference on Language Resources and Evaluation, LREC 2014, 2362-2369. https://doi.org/10.1184/R1/6373136.v1 [Google Scholar]
- UNESCO. World Arabic language day, Dec 2019. https://www.unesco.org/ar/days/world-arabic-language [Google Scholar]
- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., … & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30. [Google Scholar]
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