ItemConference Paper

Revolutionizing Legal Intelligence: Advances in Neural Networks and Language Models for Legal NLP

Title
Revolutionizing Legal Intelligence: Advances in Neural Networks and Language Models for Legal NLP
Creator
Noronha, Raoul Samuel; Alenchery, Alex Stanley; Deepa, S.; Jayapriya, J.; Vinay, M.
Description
As the legal field continues to generate vast amounts of complex text, from contracts to court rulings, machine learning and natural language processing (NLP) techniques have emerged as valuable tools to help analyze and organize this data. In this paper, a number of state-of-the-art models will be reviewed and evaluated, including transformer models like BERT, GPT, and T5, and neural network models such as LSTM and CNN-RNN hybrids. These were then tested for the legal tasks of document classification, text summarization, and entity recognition. Some of the metrics used for evaluation include Accuracy, F1-Score, ROUGE, and BLEU. Advanced models, in particular large language models (LLMs), outperform the traditional methods by a large margin since they capture the niceties of legal language and structure much more completely. Meanwhile, high-quality legal datasets remain scarce, legalese remains incomprehensible to most, and the models are still relatively unexplainable. In sum, these challenges clearly call for future research in terms of data augmentation, explainable AI techniques, and more robust training methods that would allow AI-powered tools to be integrated much more effectively within lawyers' workflows to support them in their decision-making processes. 2025 IEEE.
Source
Proceedings of 2025 3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025;pp.608-616
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
BERT; Contract Clause Extraction; GPT; Large Language Models (LLMs); Legal Document Classification; Legal NLP; Legal Text Summarization; Machine Learning; Named Entity Recognition (NER); Neural Networks; Transformer Models
Coverage
Noronha R.S., Christ University, Department of Computer Science, Bengaluru, India; Alenchery A.S., Christ University, Department of Computer Science, Bengaluru, India; Deepa S., Christ University, Department of Computer Science, Bengaluru, India; Jayapriya J., Christ University, Department of Computer Science, Bengaluru, India; Vinay M., Christ University, Department of Computer Science, Bengaluru, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833152389-3;
Format
online
Language
English
Type
Conference paper

Citation

Noronha, Raoul Samuel; Alenchery, Alex Stanley; Deepa, S.; Jayapriya, J.; Vinay, M., “Revolutionizing Legal Intelligence: Advances in Neural Networks and Language Models for Legal NLP,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26179.

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