Abstract
In this paper, we applied Recurrent Neural Networks (RNNs) Language Model on Arabic Language by training and testing it on “Arab World Books” and “Hindawi” free Arabic text datasets. While the standard architecture of RNNs does not match ideally with Arabic, we adapted a RNN model to deal with Arabic features. Our proposition in this paper is a gated Long-Short Term Memory (LSTM) model responding to some Arabic language criteria. As originality of the paper, we demonstrate the power of our LSTM model in generating Arabic text comparing to the standard LSTM model. Our results, comparing to English and Chinese text generation, have been promising and gave sufficient accuracy.
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Notes
- 1.
Arab World Books is a cultural club and Arabic bookstore that aims to promote Arab thought, provide a public service for writers and intellectuals, and exploit the vast potential of the Internet to open a window in which the world looks at Arab thought, to identify its creators and thinkers, and to achieve intellectual communication between the people of this homeland and abroad.
- 2.
Hindawi Foundation is a non-profit organization that seeks to make a significant impact on the world of knowledge. The Foundation is also working to create the largest Arabic library containing the most important books of modern Arab heritage after reproduction, to keep them from extinction.
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Souri, A., El Maazouzi, Z., Al Achhab, M., El Mohajir, B.E. (2018). Arabic Text Generation Using Recurrent Neural Networks. In: Tabii, Y., Lazaar, M., Al Achhab, M., Enneya, N. (eds) Big Data, Cloud and Applications. BDCA 2018. Communications in Computer and Information Science, vol 872. Springer, Cham. https://doi.org/10.1007/978-3-319-96292-4_41
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DOI: https://doi.org/10.1007/978-3-319-96292-4_41
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