Arabic Text Generation Using Recurrent Neural Networks

被引：5

作者：

Souri, Adnan ^{[1
]}

El Maazouzi, Zakaria ^{[1
]}

Al Achhab, Mohammed ^{[1
]}

Eddine El Mohajir, Badr ^{[1
]}

机构：

[1] Abdelmalek Essaadi Univ, New Trend Technol Team, Natl Sch Appl Sci, Tetouan, Morocco

来源：

BIG DATA, CLOUD AND APPLICATIONS, BDCA 2018 | 2018年 / 872卷

关键词：

Arabic NLP; Recurrent Neural Networks; Text generation;

D O I：

10.1007/978-3-319-96292-4_41

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：523 / 533

页数：11

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