A literature review on natural language processing techniques for Qur’anic studies: challenges and insights - Al-Shidi, Alia; Nordin, Farah Hani Bt; Mohamed, Izzeldin I.; Younis, M. A.; Elmutasim, Imadeldin Elsayed; Abusham, Eimad Eldin (Frontiers in Signal Processing, 2025)
Studies related to the Qur’an have unearthed a multitude of unique challenges and opportunities within the Natural Language Processing (NLP) research domain. These challenges tend to stem from the distinctive orthography of the Qur’an, the need for technical and linguistic support, as well as the requirement for advanced NLP technologies to address stumbling intricacies. As such, this paper delves into the significance of NLP in analyzing the holy Qur’an across ten notable dimensions; Grammatical NPL analysis, speech Recognition, Semantic Work, Ontology Base Work, Translation, Search system, Classifier system, Topic Extraction, knowledge Extraction, and Maqams. In particular, this paper looks into the following aspects - linguistic structure, positive discourse, and the use of conjunctions in translation. The probabilistic topic modeling was also deployed, along with paragraph vectors and K-Mean clustering techniques, to explore the semantically rich subjects identified across the Qur’an. Besides enabling researchers to apply deep learning techniques when analyzing Qur’anic passages and recitations, the listed methods map out the influence of ancient Arab philosophers on the development of both the Maqam theory and practice. Having said the above, NLP methods are indeed applicable for Qur’anic research purposes to unveil rich information waiting to be discovered.