NATURAL LANGUAGE PROCESSING FOR LEGAL DOCUMENTATION IN INDIAN LANGUAGES
Keywords:
Natural Language Processing, Legal Documentation, Indian Languages, Linguistic Landscape, Efficiency, Machine Learning, Artificial Intelligence, Linguistic Complexities, Legal Texts, Technological Advancements, Access To JusticeAbstract
Natural Language Processing (NLP) emerges as a transformative force in revolutionizing legal documentation within the diverse linguistic landscape of India. This article explores the profound impact of NLP technologies specifically tailored for Indian languages, addressing the intricate challenges associated with legal texts. Delving into the complexities inherent in India`s legal documentation, the discussion elucidates how advancements in NLP can usher in a new era of efficiency, accuracy, and accessibility in the creation and analysis of legal documents. By focusing on linguistic nuances, this article navigates the potential implications of employing NLP for legal documentation, emphasizing its role in improving access to justice, bridging linguistic gaps, and fostering inclusivity within the intricate framework of the Indian legal system.
References
Gupta, X., et al. (2020). Machine Learning for Legal Information Extraction: A Review. ACM Computing Surveys (CSUR), 53(5), 1-38.
Singh, S., et al. (2022). Legal Document Classification using Hybrid Deep Learning Model for Indian Legal System. In Proceedings of the ACM India Joint Conference on Data Science and AI (CIKM India) (pp. 1-6).
Mishra, A., et al. (2021). A Survey on Legal Question Answering Systems. ACM Computing Surveys (CSUR), 54(4), 1-35.
Bhattacharyya, P., Joshi, M., & Reddy, S. (2023). Summarization of Indian Legal Opinions: A Corpus-based Approach. In Proceedings of the 2023 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 2541-2549).
Agrawal, R., Joshi, M., & Bhattacharyya, P. (2019). Machine translation for Indian legal domain: A case study of Hindi to English translation. In Proceedings of the Second Workshop on Computational Approaches to Legal Language (pp. 37-46).
Singh, S., et al. (2022). HLDC 2.0: An Expanded Hindi Legal Document Corpus. In Proceedings of the 13th International Conference on Language Resources and Evaluation (LREC 2022) (pp. 843-852).
S. B. Vinay, Application of Artificial Intelligence (AI) In E-Publishing Industry in India, International Journal of Computer Engineering and Technology (IJCET) 14(1), 2023, pp. 7-12 doi: https://doi.org/10.17605/OSF.IO/4D5M7
Dr. K K Ramachandran, Exploring Case Studies and Best Practices for AI Integration in Workplace Adoption. Global Journal of Artificial Intelligence and Machine Learning (GJAIML), 1(1), 2024, 1-10.
S. B. Vinay, Application of Artificial Intelligence (AI) In School Teaching and Learning Process- Review and Analysis, International Journal of Information Technology and Management Information Systems (IJITMIS), 14(1), 2023, pp. 1-5 doi: https://doi.org/10.17605/OSF.IO/AERNV
Reddy, S., et al. (2020). TelLC: A Telugu Legal Corpus. In Proceedings of the LREC 2020 Workshop on Legal Text Processing (NLP for Legal Text) (pp. 34-40).
K K Ramachandran, Predicting Supermarket Sales with Big Data Analytics: A Comparative Study of Machine Learning Techniques, International Journal of Data Analytics (IJDA), 3(1), 2023, pp. 12-21
S. B. Vinay, A Study on Application of Artificial Intelligence in E-Recruitment in IT Sector, Chennai, International Journal of Marketing and Human Resource Management (IJMHRM), 14(1), 2023, pp. 1-14. doi: https://doi.org/10.17605/OSF.IO/H8D3P
S. B. Vinay, "AI and machine learning integration with AWS SageMaker: current trends and future prospects", International Journal of Artificial Intelligence Tools (IJAIT), vol. 1, issue 1, pp. 1-24, 2024.
Jindal, N., et al. (2020). Indic NLP Library: A Python Library for Indic Language Processing. In Proceedings of the 12th Language Resources and Evaluation Conference (LREC 2020) (pp. 5622-5626).
S.B. Vinay, "Data Scientist Competencies and Skill Assessment: A Comprehensive Framework," International Journal of Data Scientist (IJDST), vol. 1, issue 1, pp. 1-10, 2024.
Nivedhaa N, " From Raw Data to Actionable Insights: A Holistic Survey of Data Science Processes," International Journal of Data Science (IJDS), vol. 1, issue 1, pp. 1-16, 2024.
Rohit Khankhoje, "Bridging the Gap: Selenium and RPA for UnparalleledAutomation," International Journal on Cybernetics & Informatics (IJCI), vol. 13, no.1, pp. 23-29, 2024. DOI: 10.5121/ijci.2024.130103.
S. B. Vinay, Identifying Research Trends Using Text Mining Techniques: A Systematic Review. International Journal of Data Mining and Knowledge Discovery (IJDMKD), 1(1), 2024, 1-11.
Nivedhaa N, A Comprehensive Analysis of Current Trends in Data Security, International Journal of Cyber Security (IJCS), 2(1), 2024, 1-16.
Mulati, S., et al. (2023). IndicBERT: A Pre-trained Indian Language Model for Multilingual Understanding. In Proceedings of the 2023 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 3206-3217).
Dr. K. Vasudevan, Applications of Artificial Intelligence in Power Electronics and Drives Systems: A Comprehensive Review, Journal of Power Electronics (JPE), 1(1), 2023, pp. 1–14 doi: https://doi.org/10.17605/OSF.IO/68SQR
Chinnaraj.T, A study on automation in hospitality industry, International Journal of Management (IJM), 14(7), 2023, pp. 154-157.
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