Natural Language Processing for Automated Legal Document Analysis
Article Sidebar
Main Article Content
Abstract
Legal document analysis is often time-consuming and complex. This research introduces an NLP-based system for automating the extraction and classification of legal clauses. Using transformer-based models such as BERT, the system identifies key risks, obligations, and compliance issues in contracts. The proposed solution improves efficiency and reduces human error in legal analysis. The results demonstrate the potential of NLP in transforming legal workflows and enhancing access to justice.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
References
Asopa, P., Purohit, P., Nadikattu, R. R., & Whig, P. (2021). Reducing carbon footprint for sustainable development of smart cities using IoT. In Proceedings of the Third International Conference on Intelligent Communication Technologies.
Whig, P., Velu, A., & Nadikattu, R. R. (2022). The economic impact of AI-enabled blockchain in 6G-based industry. In AI and blockchain technology in 6G wireless network (pp. 205–224).
Whig, P., Velu, A., & Nadikattu, R. R. (2022). Blockchain platform to resolve security issues in IoT and smart networks. In AI-enabled agile internet of things for sustainable FinTech ecosystems (pp. 46–65).
Vaddadi, S. A., Vallabhaneni, R., & Whig, P. (2023). Utilizing AI and machine learning in cybersecurity for sustainable development through enhanced threat detection and mitigation. International Journal of Sustainable Development Through AI, ML and IoT, 2(2).
Vemulapalli, G., Yalamati, S., Palakurti, N. R., Alam, N., Samayamantri, S., & Whig, P. (2024). Predicting obesity trends using machine learning from big data analytics approach. In Proceedings of the Asia Pacific Conference on Innovation in Technology (APCIT) (pp. 1–5).
Whig, P., Kasula, B. Y., Yathiraju, N., Jain, A., & Sharma, S. (2024). Revolutionizing gender-specific healthcare: Harnessing deep learning for transformative solutions. In Transforming gender-based healthcare with AI and machine learning (pp. 14–26).