A Proposed Arabic Grammatical Error Detection Tool Based on Deep Learning | Zendy

Nora Madi | Zendy; Hend S. AlKhalifa | Zendy

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A Proposed Arabic Grammatical Error Detection Tool Based on Deep Learning

Author(s) -

Nora Madi,

Hend S. AlKhalifa

Publication year - 2018

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2018.10.482

Subject(s) - computer science , arabic , grammar , natural language processing , modern standard arabic , artificial intelligence , semitic languages , confusion , quality (philosophy) , linguistics , psychology , philosophy , epistemology , psychoanalysis

Modern Standard Arabic (MSA) is the official language in the Arab world. It is a Semitic language that has rich and complex grammar. Arabic grammar rules are numerous and complicated and might cause confusions to both Arabic natives and learners. This confusion contributes to the proliferation of errors in written Arabic texts. Developing a system for automating error detection in Arabic writing could help produce better quality text. This paper presents a work-in-progress project for developing a web-based tool that performs Arabic Grammatical error detection by employing a deep learning model.

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