Arabic Text Generation Using Recurrent Neural Networks

2018 ◽  
pp. 523-533 ◽  
Author(s):  
Adnan Souri ◽  
Zakaria El Maazouzi ◽  
Mohammed Al Achhab ◽  
Badr Eddine El Mohajir
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Arabic Text ◽  
Text Generation

scholarly journals From Imitation to Prediction, Data Compression vs Recurrent Neural Networks for Natural Language Processing

2018 ◽  
Vol 21 (61) ◽  
pp. 30 ◽  
Author(s):  
Juan Andres Laura ◽  
Gabriel Omar Masi ◽  
Luis Argerich
Keyword(s):  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Data Compression ◽  
Language Processing ◽  
Recurrent Neural Networks ◽  
Compression Algorithm ◽  
Text Generation ◽  
Generative Processes ◽  
Automatic Text

In recent studies Recurrent Neural Networks were used for generative processes and their surprising performance can be explained by their ability to create good predictions. In addition, Data Compression is also based on prediction. What the problem comes down to is whether a data compressor could be used to perform as well as recurrent neural networks in the natural language processing tasks of sentiment analysis and automatic text generation. If this is possible, then the problem comes down to determining if a compression algorithm is even more intelligent than a neural network in such tasks. In our journey, a fundamental difference between a Data Compression Algorithm and Recurrent Neural Networks has been discovered.

scholarly journals Neural network dealing with Arabic language

2020 ◽  
Vol 9 (2) ◽  
pp. 73
Author(s):  
Adnan Souri ◽  
Mohammed Al Achhab ◽  
Badr Eddine Elmohajir ◽  
Abdelali Zbakh
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Arabic Language ◽  
Language Modeling ◽  
Arabic Text ◽  
Text Generation ◽  
The Standard Model ◽  
Artificial Neural ◽  
Text Prediction ◽  
Research Domains

Artificial Neural Networks have proved their efficiency in a large number of research domains. In this paper, we have applied Artificial Neural Networks on Arabic text to prove correct language modeling, text generation, and missing text prediction. In one hand, we have adapted Recurrent Neural Networks architectures to model Arabic language in order to generate correct Arabic sequences. In the other hand, Convolutional Neural Networks have been parameterized, basing on some specific features of Arabic, to predict missing text in Arabic documents. We have demonstrated the power of our adapted models in generating and predicting correct Arabic text comparing to the standard model. The model had been trained and tested on known free Arabic datasets. Results have been promising with sufficient accuracy.

Automatic diacritization of Arabic text using recurrent neural networks

2015 ◽  
Vol 18 (2) ◽  
pp. 183-197 ◽  
Author(s):  
Gheith A. Abandah ◽  
Alex Graves ◽  
Balkees Al-Shagoor ◽  
Alaa Arabiyat ◽  
Fuad Jamour ◽  
...  
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Arabic Text
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