{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/hadiths-classification-using-a-novel-author","title":"Hadiths Classification Using a Novel Author-Based Hadith Classification Dataset (ABCD)","arxiv_id":null,"date":"2023-08-14","proceeding":"Big Data Cogn. Comput. 2023 8","authors":["Ahmed Ramzy","Marwan Torki","Mohamed Abdeen","Omar Saif","Mustafa ElNainay","AbdAllah Alshanqiti","Emad Nabil"],"abstract":"Religious studies are a rich land for Natural Language Processing (NLP). The reason is\r\nthat all religions have their instructions as written texts. In this paper, we apply NLP to Islamic\r\nHadiths, which are the written traditions, sayings, actions, approvals, and discussions of the Prophet\r\nMuhammad, his companions, or his followers. A Hadith is composed of two parts: the chain of\r\nnarrators (Sanad) and the content of the Hadith (Matn). A Hadith is transmitted from its author to a\r\nHadith book author using a chain of narrators. The problem we solve focuses on the classification\r\nof Hadiths based on their origin of narration. This is important for several reasons. First, it helps\r\ndetermine the authenticity and reliability of the Hadiths. Second, it helps trace the chain of narration\r\nand identify the narrators involved in transmitting Hadiths. Finally, it helps understand the historical\r\nand cultural contexts in which Hadiths were transmitted, and the different levels of authority\r\nattributed to the narrators. To the best of our knowledge, and based on our literature review, this\r\nproblem is not solved before using machine/deep learning approaches. To solve this classification\r\nproblem, we created a novel Author-Based Hadith Classification Dataset (ABCD) collected from\r\nclassical Hadiths’ books. The ABCD size is 29 K Hadiths and it contains unique 18 K narrators, with\r\nall their information. We applied machine learning (ML), and deep learning (DL) approaches. ML\r\nwas applied on Sanad and Matn separately; then, we did the same with DL. The results revealed that\r\nML performs better than DL using the Matn input data, with a 77% F1-score. DL performed better\r\nthan ML using the Sanad input data, with a 92% F1-score. We used precision and recall alongside the\r\nF1-score; details of the results are explained at the end of the paper. We claim that the ABCD and the\r\nreported results will motivate the community to work in this new area. Our dataset and results will\r\nrepresent a baseline for further research on the same problem.","url_abs":"https://www.mdpi.com/2504-2289/7/3/141","url_pdf":"https://www.mdpi.com/2504-2289/7/3/141","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"hadiths-classification-using-a-novel-author","repo_url":"https://github.com/emadnabilcs/ABCD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"16k","task_name":"16k"},{"task_slug":"classification-1","task_name":"Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}