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Pertanika Journal of Science & Technology

Official journal of Universiti Putra Malaysia for scholarly work across science, engineering and related technologies.

e-ISSN 2231-8526 ISSN 0128-7680
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Research article

Automated Islamic Jurisprudential Legal Opinions Generation Using Artificial Intelligence

Amr Abdullah Munshi, Wesam Hasan AlSabban, Abdullah Tarek Farag, Omar Essam Rakha, Ahmad Al Sallab and Majid Alotaibi

Volume 30, Issue 2 April 2022 Published 1 April 2022
https://doi.org/10.47836/pjst.30.2.16
KeywordsArtificial intelligence, Islamic fatwa, machine learning, natural language processing, question answering, text classification
Article content

Abstract

Islam is the second-largest and fastest-growing religion. The Islamic Law, Sharia, represents a profound component of the day-to-day lives of Muslims. While sources of Sharia are available for anyone, it often requires a highly qualified person, the Mufti, to provide Fatwa. With Islam followers representing almost 25% of the planet earth population, generating many queries, and the sophistication of the Mufti qualification process, creating a shortage in them, we have a supply-demand problem, calling for Automation solutions. This scenario motivates the application of Artificial Intelligence (AI) to Automated Islamic Fatwa in a scalable way that can adapt to various sources like social media. In this work, the potential of AI, Machine Learning, and Deep Learning, with technologies like Natural Language Processing (NLP), paving the way to help the Automation of Islam Fatwa are explored. The work started by surveying the State-of-The-Art (SoTA) of NLP and exploring the potential use-cases to solve the problems of Question answering and Text Classification in the Islamic Fatwa Automation. The first and major enabler component for AI application for Islamic Fatwa, the data were presented by building the largest dataset for Islamic Fatwa, spanning the widely used websites for Fatwa. Moreover, the baseline systems for Topic Classification, Topic Modeling, and Retrieval-based Question-Answering are presented to set the future research and benchmark on the dataset. Finally, the dataset is released and baselines to the public domain to help advance future research in the area.
Supporting literature

References

  1. Abdi, A., Hasan, S., Arshi, M., Shamsuddin, S. M., & Idris, N. (2020). A question answering system in hadith using linguistic knowledge. Computer Speech & Language, 60, Article 101023. https://doi.org/10.1016/j.csl.2019.101023
  2. Al Sallab, A. A., Baly, R., Badaro, G., Hajj, H. M., El-Hajj, W., & Shaban, K. (2015a, March 9-10). Towards deep learning models for sentiment analysis in Arabic. In Machine Learning and Data Analytics Symposium - MLDAS 2015 (pp. 1-5). Doha, Qatar. https://doi.org/10.18653/v1/W15-3202
  3. Al Sallab, A., Hajj, H., Badaro, G., Baly, R., El-Hajj, W., & Shaban, K. (2015b, July 26-31). Deep learning models for sentiment analysis in Arabic. In Proceedings of the Second Workshop on Arabic Natural Language Processing (pp. 9-17). Beijing, China. https://doi.org/10.18653/v1/W15-3202
  4. Al Sallab, A., Rashwan, M., Raafat, H., & Rafea, A. (2014, October 25). Automatic Arabic diacritics restoration based on deep nets. In Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP) (pp. 65-72). Doha, Qatar. https://doi.org/10.3115/v1/W14-3608
  5. Al-Sallab, A., Baly, R., Hajj, H., Shaban, K. B., El-Hajj, W., & Badaro, G. (2017). Aroma: A recursive deep learning model for opinion mining in arabic as a low resource language. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), 16(4), 1-20. https://doi.org/10.1145/3086575
  6. Antoun, W., Baly, F., & Hajj, H. (2020). Arabert: Transformer-based model for arabic language understanding. arXiv Preprint.
  7. Athiwaratkun, B., Wilson, A. G., & Anandkumar, A. (2018). Probabilistic fasttext for multi-sense word embeddings. arXiv Preprint. https://doi.org/10.18653/v1/P18-1001
  8. Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv Preprint.
  9. Baly, R., Badaro, G., Hamdi, A., Moukalled, R., Aoun, R., El-Khoury, G., El-Sallab, A., Hajj, H., Habash, N., Shaban, K. B., & El-Hajj, W. (2017). Omam at semeval-2017 task 4: Evaluation of English state-of-the-art sentiment analysis models for Arabic and a new topic-based model. In Proceedings of the 11th International Workshop on Semantic Evaluation (SEMEVAL-2017) (pp. 603-610). ACM Publishing. https://doi.org/10.18653/v1/S17-2099
  10. Baly, R., Hobeica, R., Hajj, H., El-Hajj, W., Shaban, K. B., & Al-Sallab, A. (2016). A meta-framework for modeling the human reading process in sentiment analysis. ACM Transactions on Information Systems (TOIS), 35(1), 1-21. https://doi.org/10.1145/2950050
  11. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., … & Amodei, D. (2020). Language models are few-shot learners. arXiv Preprint.
  12. Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv Preprint.
  13. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv Preprint.
  14. Djandji, M., Baly, F., Antoun, W., & Hajj, H. (2020). Multi-task learning using AraBert for offensive language detection. In Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection (pp. 97-101). European Language Resource Association.
  15. Essam, O. (2017). Arabic AskFM dataset. Data Science.
  16. Hamoud, B., & Atwell, E. (2016). Quran question and answer corpus for data mining with WEKA. In 2016 Conference of Basic Sciences and Engineering Studies (SGCAC) (pp. 211-216). IEEE Publishing. https://doi.org/10.1109/SGCAC.2016.7458032
  17. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
  18. Howard, J., & Ruder, S. (2018). Universal language model fine-tuning for text classification. arXiv Preprint. https://doi.org/10.18653/v1/P18-1031
  19. Luong, M. T., Pham, H., & Manning, C. D. (2015). Effective approaches to attention-based neural machine translation. arXiv Preprint. https://doi.org/10.18653/v1/D15-1166
  20. Magooda, A., Sayed, A. M., Mahgoub, A. Y., Ahmed, H., Rashwan, M., Raafat, H., Kamal, E., & & Al Sallab, A. A. (2016). RDI_Team at SemEval-2016 Task 3: RDI unsupervised framework for text ranking. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016) (pp. 822-827). ACM Publishing. https://doi.org/10.18653/v1/S16-1127
  21. Nada, A. M. A., Alajrami, E., Al-Saqqa, A. A., & Abu-Naser, S. S. (2020). Arabic text summarization using AraBERT model using extractive text summarization approach. International Journal of Academis Information Systems Research, 4(8), 6-9.
  22. Peters, M. E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018). Deep contextualized word representations. arXiv Preprint. https://doi.org/10.18653/v1/N18-1202
  23. Rashwan, M. A., Al Sallab, A. A., Raafat, H. M., & Rafea, A. (2015). Deep learning framework with confused sub-set resolution architecture for automatic Arabic diacritization. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 23(3), 505-516. https://doi.org/10.1109/TASLP.2015.2395255
  24. Sihotang, M. T., Jaya, I., Hizriadi, A., & Hardi, S. M. (2020). Answering Islamic questions with a chatbot using fuzzy string-matching algorithm. In Journal of Physics: Conference Series (Vol. 1566, No. 1, p. 012007). IOP Publishing. https://doi.org/10.1088/1742-6596/1566/1/012007
  25. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. In 31st Conference on Neural Information Processing Systems (NIPS 2017) (pp. 5998-6008). Long Beach, USA.
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