اللُّغة العربية والتحوّل الرَّقْمي: قراءة في أثر الذّكاء الاصطناعيّ

ʿAbd al-Tawwāb, Ramaḍān. Buḥūth wa Maqālāt fī al-Lugha (Studies and Articles in Language). Cairo/Riyadh: Maktabat al-Khānjī and Dār al-Rifāʿī, 1982.

Abū Jabbāra, Amjad, Aḥmad al-Ḥāyik, et al. Taṭbīqāt al-Dhakāʾ al-Iṣṭināʿī fī Khidmat al-Lugha al-ʿArabiyya (Artificial Intelligence Applications in the Service of the Arabic Language). Edited by Yūsuf al-ʿAryān. Riyadh: King Abdullah bin Abdulaziz International Center for the Arabic Language, 2019.

Al Khatib, M., & Shaalan, K. “The Key Challenges for Arabic Machine Translation”. In: Intelligent Natural Language Processing: Trends and Applications. Studies in Computational Intelligence, (2018): pp. 139-156.

Alagar, R. "The Role of Natural Language Processing (NLP) in AI Applications". Skill floor, (2023, october 2) Retrieved from: https://skillfloor.com/blog/the-role-ofnaturallanguageprocessing-nlp-in-ai-applications.

Alasmary, Faris, Zaafarani, Orjuwan, Ghannam, Ahmad. “CATT: Character-based Arabic Tashkeel Transformer“. Association for Computational Linguistics. Bangkok, Thailand. (2024). arXiv:2407.03236v1

al-Ghuḍayya, Iʿtidāl bint Muḥammad. “al-Jumla al-ʿArabiyya: Anmāṭuhā wa Taḥawwulātuhā (The Arabic Sentence: Its Patterns and Transformations).” Journal of Humanities and Nature, vol. 3, no. 10 (2022): 494–522.

al-Jurjānī, ʿAbd al-Qāhir. Dalāʾil al-Iʿjāz fī ʿIlm al-Maʿānī (Proofs of Inimitability in the Science of Meanings). Edited by Maḥmūd Muḥammad Shākir. Cairo: Maktabat al-Khānjī, 1992.

al-Nābulusī, Muḥammad. “Taḥaddiyāt Muʿālajat al-Lugha al-ʿArabiyya fī Taṭbīqāt al-Dhakāʾ al-Iṣṭināʿī (Challenges of Arabic Language Processing in AI Applications).” Journal of Modern Linguistic Studies, vol. 12, no. 4 (2024): 215–245.

al-Qunayʿīr, Fāris, et al. Khawārizmiyyāt al-Dhakāʾ al-Iṣṭināʿī fī Taḥlīl al-Naṣṣ al-ʿArabī (AI Algorithms in Arabic Text Analysis). Edited by ʿAbd Allāh al-Fayfī. Riyadh: King Abdullah bin Abdulaziz International Center for the Arabic Language, 2019.

al-Sāmarrāʾī, Fāḍil. Maʿānī al-Naḥw (Meanings of Grammar). Jordan: Dār al-Fikr, 2000.

al-Sāmarrāʾī, Ibrāhīm. al-Fiʿl: Zamānuhu wa Abniyatuhu (The Verb: Its Tense and Forms). Beirut: Muʾassasat al-Risāla, 2013.

Anīs, Ibrāhīm. Min Asrār al-Lugha (Secrets of the Language). 6th ed. Cairo: Maktabat al-Anjلو al-Miṣriyya.

Antoun, W., Baly, F., & Hajj, H. “AraBERT: Transformer-Based Model for Arabic Language Understanding”. Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection, Marseille: European Language Resource Association, (2020): pp. 9-15.

ʿAṭiyya, Muḥammad, Aḥmad Rāghib, et al. al-ʿArabiyya wa al-Dhakāʾ al-Iṣṭināʿī (Arabic and Artificial Intelligence). Edited by al-Muʿtazz bi-llāh al-Saʿīd. Riyadh: King Abdullah bin Abdulaziz International Center for the Arabic Language, 2019.

Attia, M. “Arabic Tokenization System”. Proceedings of the 2007 Workshop on Computational Approaches to Semitic Languages, Prague: Association for Computational Linguistics ,(2007): pp. 65-72.

Blodgett, S., Barocas, S., Daumé III, H., & Wallach, H. “Language (Technology) is Power: A Critical Survey of 'Bias' in NLP”. arXiv, (2020): pp. 1-23. Retrieved from: https://arxiv.org/abs/2005.14050.

Chowdhary, K. R. “Fundamentals of Artificial Intelligence”. New Delhi: Springer, (2020).

Darwish, K., & Mubarak, H. “Farasa: A Fast and Furious Segmenter for Arabic”. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, San Diego, California: Association for Computational Linguistics (2016): pp. 11-16.

Elkateb, S., Black, W., Rodríguez, H., Alkhalifa, M., Vossen, P., Pease, A., & Fellbaum, C. “Building a WordNet for Arabic”. Proceedings of the Fifth International Conference on Language Resources and Evaluation, Genoa, Italy: European Language Resources Association ,(2006): pp. 29-34.

Farghaly, A., & Shaalan, K. “Arabic Natural Language Processing: Challenges and Solutions”. ACM Transactions on Asian Language Information Processing, 8(4), (2009): Article 14, pp. 1-22.

Fix Ai Editor, F. A. “Revolutionizing Language Translation: Google’s Neural Machine Translation System”. FXIS.ai, (6 September 2024). Retrieved from: https://fxis.ai/edu/revolutionizinglanguage-translation-googles-neural-machine-translation-system/.

Habash, N. “Introduction to Arabic Natural Language Processing”. (San Rafael, CA: Morgan & Claypool Publishers, 2010).

Ḥasan, ʿAbbās. al-Naḥw al-Wāfī (Comprehensive Grammar). 15th ed. Egypt: Dār al-Maʿārif, 2010.

Ḥassān, Tammām. al-Lugha al-ʿArabiyya: Maʿnāhā wa Mabnāhā (The Arabic Language: Its Meaning and Structure). Cairo: ʿĀlam al-Kutub, 2006.

Ibn Jinnī, ʿUthmān. al-Khaṣāʾiṣ (The Characteristics). Edited by Muḥammad ʿAlī al-Najjār. Cairo: Dār al-Kutub al-Miṣriyya, 1952.

Ibn Jinnī, ʿUthmān. al-Munṣif (The Fair One). Egypt: Dār Iḥyāʾ al-Turāth al-Qadīm, 1954.

Jurafsky, Daniel, & James H. Martin. “Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition”. 3rd ed., Stanford: Stanford University Press, (2024).

Kalluri, S. “Deep Learning Based Sentiment Analysis”. Master Thesis, (Sweden: Blekinge Institute of Technology, 2023).

Kathare, A., Reddy, P. V., & Prabhu, S. “Analysis of Elasticsearch in Comparison with MongoDB and Hadoop”. International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC), Mysore, India ,(2017): pp. 717-722.

Khalati, M., & Al-Romany, S. “Dialectal Arabic Processing: Current State and Future Directions”. International Journal of Advanced Computer Science and Applications, 11(8), (2020): pp. 920-928.

Kharbāṭ, ʿAlī Ṭarkhān. “Taʿaddud Waẓīfat al-Adāt al-Naḥwiyya fī al-Qurʾān al-Karīm wa Atharuh fī al-Maʿnā (Multiplicity of Grammatical Particle Functions in the Qurʾān).” Majallat al-Qādisiyya fī al-Ādāb wa al-ʿUlūm al-Tarbawiyya, no. 3, pt. 1 (2023): 1–30.

Luger, E., & Sellen, A. “Like Having a Really Bad PA: The Gulf between User Expectation and Experience of Conversational Agents”. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, (2016): pp. 5286-5297.

Manning, C. D., & Schütze, H. “Foundations of Statistical Natural Language Processing”. (Cambridge, MA: MIT Press, 1999).

Manohar, V., Povey, D., & Khudanpur, S. “JHU Kaldi System for Arabic MGB-3 ASR Challenge Using Diarization, Audio-Transcript Alignment and Transfer Learning”. IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), Okinawa, Japan,(2017): pp. 346-352.

Medhat, W., Hassan, A., & Korashy, H. “Sentiment Analysis Algorithms and Applications: A Survey”. Ain Shams Engineering Journal, (2014): pp. 1-22.

Mikolov, T., Chen, K., Corrado, G., & Dean, J. “Efficient Estimation of Word Representations in Vector Space”. arXiv preprint, (2013). Retrieved from: https://arxiv.org/abs/1301.3781.

Minsky, Marvin, ed. “Semantic Information Processing”. (Cambridge, MA: MIT Press, 1968).

Murhaf, Fares and Touileb Samia. "BabelBot at AraFinNLP2024: Fine-Tuning T5 for Multi-Dialect Intent Detection with Synthetic Data and Model Ensembling." In Proceedings of ArabicNLP,. Bangkok, Thailand: Association for Computational Linguistics, (2024): pp. 433–440. https://aclanthology.org/2024.arabicnlp-1.40.

Nallapati, R., Zhou, B., dos Santos, C., Gu̇lçehre, Ç., & Xiang, B. “Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond”. Conference on Computational Natural Language Learning, (Berlin: Association for Computational Linguistics ,2016): pp. 280-290.

Nassif, A. B., Elnagar, A., Shahin, I., & Henno, S. “Deep Learning for Arabic Subjective Sentiment Analysis: Challenges and Research Opportunities”. ScienceDirect, (January 2021). Retrieved from: https://www.sciencedirect.com/science/article/abs/pii/S1568494620307742.

Obeid, O., Zalmout, N., Khalifa, S., Taji, D., Oudah, M., Alhafni, B., Go, F., Inoue, N., Eryani, F., Erdmann, A., & Habash, N. “CAMeL Tools: An Open Source Python Toolkit for Arabic Natural Language Processing”. Proceedings of the 12th Language Resources and Evaluation Conference, Marseille, France: European Language Resources Association (2020): pp. 7022-7032.

Otten, M. “Advanced Natural Language Processing with spaCy: A Guide to Understanding and Building NLP Applications”. (Birmingham, UK: Packt Publishing, 2023).

Palanivinayagam, A., El-Bayeh, C. Z., & Damaševičius, R. “Twenty Years of Machine Learning-Based Text Classification: A Systematic Review”. Algorithms, (2023): pp. 1-28.

Pasha, A., Al-Badrashiny, M., Diab, M., El Kholy, A., Eskander, R., Habash, N., Pooleery, M., Rambow, O., & Roth, R. “MADAMIRA: A Fast, Comprehensive Tool for Morphological Analysis and Disambiguation of Arabic”. Proceedings of the Ninth International Conference on Language Resources and Evaluation, Reykjavik, Iceland: European Language Resources Association (2014): pp.1094-1101.

Restak, M. “Cloud Natural Language API Documentation”. (Mountain View, California: Google Cloud, 2025). Retrieved from: https://cloud.google.com/natural-language/docs.

Sarawagi, S. “Information Extraction”. Foundations and Trends® in Databases, (2008).

Sengupta, Neha, Sunil Kumar Sahu, Bokang Jia, et al. “Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models.” arXiv, September 29 (2023). https://doi.org/10.48550/arXiv.2308.16149.

spaCy: Industrial-Strength NLP. “spaCy Usage Documentation”. (Berlin: Explosion AI, 2025). Retrieved from: https://spacy.io/usage.

Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., … Dean, J. “Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation”. Computer Science, (2016): pp. 1-23.

Zhang, L., Wang, S., & Liu, B. “Deep Learning for Sentiment Analysis: A Survey”. Wiley Interdisciplinary Reviews, (2018): pp. 1-25.