Deep learning model for identifying the arabic language learners based on gated recurrent unit network

Seifeddine Mechti, Roobaea Alroobaea, Moez Krichen, Saeed Rubaiee, Anas Ahmed

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

This paper focuses on identifying the Arabic Language 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 ngrams 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.

Original languageEnglish
Pages (from-to)620-627
Number of pages8
JournalInternational Journal of Advanced Computer Science and Applications
Volume11
Issue number5
DOIs
StatePublished - 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2020 Science and Information Organization.

Keywords

  • Arabic
  • Deep learning
  • Gated recurrent unit network (GRUN)
  • Native language identification (NLI)

ASJC Scopus subject areas

  • General Computer Science

Fingerprint

Dive into the research topics of 'Deep learning model for identifying the arabic language learners based on gated recurrent unit network'. Together they form a unique fingerprint.

Cite this