Arabic Text Classification Based on Features Reduction Using Artificial Neural Networks

2013 ◽  
Author(s):  
F. A. Zaghoul ◽  
S. Al-Dhaheri
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Text Classification ◽  
Arabic Text ◽  
Features Reduction ◽  
Artificial Neural ◽  
Arabic Text Classification

scholarly journals Text classification using artificial neural networks

2017 ◽  
Vol 7 (1.1) ◽  
pp. 603 ◽  
Author(s):  
P Lakshmi Prasanna ◽  
Dr D.Rajeswara Rao
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Text Classification ◽  
Text Categorization ◽  
Text Documents ◽  
Artificial Neural

Text Categorization is the process of classifying the text or documents into its corresponding categories which are defined previously. As the text or data is increasing enormously now-a-days it’s not possible to classify all the text documents manually hence its necessary to use some techniques or methods to classify the text automatically. In this paper we are using the ANN technique to classify the text, the purpose of choosing it, its advantages and its process is described in the further sections.

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.

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