CNN-BiLSTM Model for Arabic Dialect Identification

Author:

Hedhli MalekORCID,Kboubi FerihaneORCID

Publisher

Springer Nature Switzerland

Reference28 articles.

1. Badri, N., Kboubi, F., Habacha Chaibi, A.: Towards automatic detection of inappropriate content in multi-dialectic Arabic text. In: Bădică, C., Treur, J., Benslimane, D., Hnatkowska, B., Krótkiewicz, M. (eds.) ICCCI 2022. CCIS, vol. 1653, pp. 84–100. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16210-7_7

2. Mousa, A.: Deep identification of Arabic dialects. Thèse de doctorat, Informatics Institute (2021)

3. Ali, A., Dehak, N., Cardinal, P.: Automatic dialect detection in Arabic broadcast speech. arXiv preprint arXiv:1509.06928 (2015)

4. Communications in Computer and Information Science;R Tachicart,2018

5. El-Haj, M., Rayson, P., Aboelezz, M.: Arabic dialect identification in the context of bivalency and code-switching. In: Proceedings of the 11th International Conference on Language Resources and Evaluation, Miyazaki, Japan, pp. 3622–3627. European Language Resources Association (2018)

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