Arabic natural language processing for Qur’anic research: a systematic review

被引:0
作者
Muhammad Huzaifa Bashir
Aqil M. Azmi
Haq Nawaz
Wajdi Zaghouani
Mona Diab
Ala Al-Fuqaha
Junaid Qadir
机构
[1] Information Technology University (ITU),Department of Electrical Engineering
[2] King Saud University (KSU),Department of Computer Science, College of Computer & Information Sciences
[3] Jamia Ashrafia,College of Humanities and Social Sciences
[4] Punjab University College of Information Technology (PUCIT),Department of Computer Science
[5] Hamad Bin Khalifa University (HBKU),Information and Computing Technology Division, College of Science and Engineering
[6] George Washington University (GW),Department of Computer Science and Engineering
[7] Hamad Bin Khalifa University (HBKU),undefined
[8] Qatar University (QU),undefined
来源
Artificial Intelligence Review | 2023年 / 56卷
关键词
Arabic natural language processing; Machine learning; Quranic NLP; Religious texts; Classical Arabic;
D O I
暂无
中图分类号
学科分类号
摘要
The Qur’an is a fourteen centuries old divine book in Arabic language that is read and followed by almost two billion Muslims globally as their sacred religious text. With the rise of Islam, the Arabic language gained popularity and became the lingua franca for large swaths of the old world. Devout Muslims read the Qur’an daily seeking guidance and comfort. Though the Qur’an, as a text, is short, there is a huge volume of supporting work filling tens of thousands of volumes, e.g., commentaries, exegesis, etc. Recently, there has been a renewed interest in such religious texts by non-specialists. Many of which were fueled by the recent advances in computational and natural language processing (NLP) techniques. These techniques help the development of tools that benefit common people to gain knowledge easily. This paper surveys the different efforts in the field of Qur’anic NLP, serving as a synthesized compendium of works (tools, data sets, approaches) covering the gamut from automated morphological analysis to correction of Qur’anic recitation via speech recognition. Multiple approaches are discussed for several tasks, where appropriate. Finally, we outline future research directions in this field.
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页码:6801 / 6854
页数:53
相关论文
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