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Research Article | Open Access |

Evaluation of Gated Recurrent Unit in Arabic Diacritization

Author 1: Rajae Moumen Author 2: Raddouane Chiheb Author 3: Rdouan Faizi Author 4: Abdellatif El Afia
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 9, No. 11 · Published 2018 · Cited by 5

DOI: https://doi.org/10.14569/IJACSA.2018.091150

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Abstract

Recurrent neural networks are powerful tools giving excellent results in various tasks, including Natural Language Processing tasks. In this paper, we use Gated Recurrent Unit, a recurrent neural network implementing a simple gating mechanism in order to improve the diacritization process of Arabic. Evaluation of Gated Recurrent Unit for diacritization is performed in comparison with the state-of-the art results obtained with Long-Short term memory a powerful RNN architecture giving the best-known results in diacritization. Evaluation covers two performance aspects, Error rate and training runtime.

Keywords

Gated recurrent unit long-short term memory arabic diacritization

How to Cite this Article

Moumen, R., Chiheb, R., Faizi, R., & Afia, A. E. (2018). Evaluation of Gated Recurrent Unit in Arabic Diacritization. International Journal of Advanced Computer Science and Applications, 9(11). https://doi.org/10.14569/IJACSA.2018.091150

Moumen, Rajae, et al.. "Evaluation of Gated Recurrent Unit in Arabic Diacritization." International Journal of Advanced Computer Science and Applications, vol. 9, no. 11, 2018, https://doi.org/10.14569/IJACSA.2018.091150.

@article{Moumen2018,
  title     = {Evaluation of Gated Recurrent Unit in Arabic Diacritization},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {11},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {Rajae Moumen and Raddouane Chiheb and Rdouan Faizi and Abdellatif El Afia},
  doi       = {10.14569/IJACSA.2018.091150},
  url       = {https://doi.org/10.14569/IJACSA.2018.091150}
}

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