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  • 标题:Deep Learning Model for Identifying the Arabic Language Learners based on Gated Recurrent Unit Network
  • 本地全文:下载
  • 作者:Seifeddine Mechti ; Roobaea Alroobaea ; Moez Krichen
  • 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)
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