This item is non-discoverable
Deep Learning for Arabic Error Detection and Correction
Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Research on tools for automating the proofreading of Arabic text has received much attention in recent years.
There is an increasing demand for applications that can detect and correct Arabic spelling and grammatical
errors to improve the quality of Arabic text content and application input. Our review of previous studies
indicates that few Arabic spell-checking research efforts appropriately address the detection and correction
of ill-formed words that do not conform to the Arabic morphology system. Even fewer systems address the
detection and correction of erroneous well-formed Arabic words that are either contextually or semantically
inconsistent within the text. We introduce an approach that investigates employing deep neural network
technology for error detection in Arabic text. We have developed a systematic framework for spelling and
grammar error detection, as well as correction at the word level, based on a bidirectional long short-term
memory mechanism and word embedding, in which a polynomial network classifier is at the top of the sys tem. To get conclusive results, we have developed the most significant gold standard annotated corpus to
date, containing 15 million fully inflected Arabic words. The data were collected from diverse text sources
and genres, in which every erroneous and ill-formed word has been annotated, validated, and manually re vised by Arabic specialists. This valuable asset is available for the Arabic natural language processing research
community. The experimental results confirm that our proposed system significantly outperforms the per formance of Microsoft Word 2013 and Open Office Ayaspell 3.4, which have been used in the literature for
evaluating similar research.