Deep Learning for Automated Legal Document Review and Analysis

Prof. Frek kant

Abstract


Legal document review is time-consuming and labor-intensive, but AI can automate this process. This paper explores the use of deep learning models for the automated review and analysis of legal documents. The system utilizes Natural Language Processing (NLP) techniques to extract key information, classify legal terms, and identify clauses that may require attention. The framework is evaluated on large legal datasets, demonstrating high accuracy in identifying critical information such as liabilities, obligations, and compliance issues. The findings highlight the potential of AI to streamline legal workflows, reduce costs, and improve efficiency in legal practices.

References


Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (pp. 1097-1105).

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.

Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. In Proceedings of the International Conference on Learning Representations (ICLR).

Murphy, K. P. (2012). Machine learning: A probabilistic perspective. MIT Press.

Ng, A. Y., & Jordan, M. I. (2002). On discriminative vs. generative classifiers: A comparison of logistic regression and naive Bayes. In Advances in Neural Information Processing Systems (pp. 841-848).

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536.

Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85-117.

Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., ... & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484-489.

Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems (pp. 5998-6008).

Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600-612.

Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8(3-4), 229-256.

Zhou, Z. H. (2012). Ensemble methods: Foundations and algorithms. Chapman & Hall/CRC.

Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2021). Leveraging Cloud Computing for Efficient Data Processing in SAP Enterprise Solutions. International Journal of Machine Learning for Sustainable Development, 3(4).

Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2021). Machine Learning in SAP Workflows: A Study of Predictive Analytics and Automation. Transactions on Latest Trends in Artificial Intelligence, 2(2).

Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2021). Machine Learning Models for Optimizing SAP-Based Data Processing in Cloud Environments. International Journal of Sustainable Development in Computing Science, 3(3).

Ranjan, P., & Dahiya, S. (2021). Advanced threat detection in api security: Leveraging machine learning algorithms. International Journal of Communication Networks and Information Security, 13(1).

Dhaiya, S., Pandey, B. K., Adusumilli, S. B. K., & Avacharmal, R. (2021) Optimizing API Security in FinTech Through Genetic Algorithm based Machine Learning Model.

Chintale, P., Korada, L., Ranjan, P., & Malviya, R. K. (2019). Adopting Infrastructure as Code (IaC) for Efficient Financial Cloud Management. ISSN: 2096-3246, 51(04).

Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2020). Optimizing SAP Data Processing with Machine Learning Algorithms in Cloud Environments. International Transactions in Artificial Intelligence, 4(4).

Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2020). Artificial Intelligence in Business Analytics: Cloud-Based Strategies for Data Processing and Integration. International Journal of Sustainable Development in Computing Science, 2(4).

Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2020). Scalable Data Processing Pipelines: The Role of AI and Cloud Computing. International Scientific Journal for Research, 2(2).


Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 International Journal of Machine Learning for Sustainable Development

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Impact Factor : 

JCR Impact Factor: 5.9 (2020)

JCR Impact Factor: 6.1 (2021)

JCR Impact Factor: 6.7 (2022)

JCR Impact Factor: 7.6 (2023)

JCR Impact Factor: 8.6 (2024)

JCR Impact Factor: Under Evaluation (2025)

A Double-Blind Peer-Reviewed Refereed Journal