文章基本信息

标题：Deep Learning Model for Identifying the Arabic Language Learners based on Gated Recurrent Unit Network
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作者：Seifeddine Mechti ; Roobaea Alroobaea ; Moez Krichen 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2020
卷号：11
期号：5
DOI：10.14569/IJACSA.2020.0110576
出版社：Science and Information Society (SAI)
摘要：This paper focuses on identifying the Arabic Lan-guage learners. The main contribution of the proposed method is to use a deep learning model based on the Gated Recurrent Unit Network (GRUN). The proposed model explores a multitude of stylistic features such as the syntax, the lexical and the n-grams ones. To the best of our awareness, the obtained results outperform those obtained by the best existing systems. Our accuracy is the best comparing with the pioneers (45% vs 41%), considering the limited data and the unavailability of accurate tools dedicated to the Arabic language.
关键词：Arabic; Native Language Identification (NLI); deep learning; Gated Recurrent Unit Network (GRUN)