Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)

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Event-Trace: AI-Driven Law Section Predictions from Case Narratives

Authors
Jaina Patel1, *, Bhumika Prajapati1
1Department of Computer Engineering, Madhuben & Bhanubhai Patel Institute of Technology, The Charutar Vidya Mandal (CVM) University, Anand, Gujarat, India
*Corresponding author. Email: j4jaina@gmail.com
Corresponding Author
Jaina Patel
Available Online 4 November 2025.
DOI
10.2991/978-94-6463-858-5_6How to use a DOI?
Keywords
AI-driven; Law section; Machine learning; Extra Trees Classifier; Case narratives
Abstract

Event-Trace is an AI-based approach that utilizes case descriptions in order to predict law section classifications based on K-Nearest Neighbors (KNN), Linear Support Vector Machine (SVM), Decision Tree, Random Forest and Extra Trees Classifier. These models were chosen because of their advantage in dealing with large quantities of legal data and diverse feature interdependencies. In classification based on proximity, we have KNN while for the linear decision boundaries, Linear SVM is the best model. Decision Trees are easy to interpret, and Random Forest reduces variance and produces more accurate results. But Extra Trees Classifier yields the highest accuracy and training speed as well as less prone to overfitting. This model surpasses the rest because it incorporates numerous, randomly chosen features that make the procedure fit to work on distinctive case narratives. The results demonstrate the benefit of applying machine learning to enhance the analyzing abilities of legal documents, providing better predictions of legal section kinds.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
Series
Advances in Computer Science Research
Publication Date
4 November 2025
ISBN
978-94-6463-858-5
ISSN
2352-538X
DOI
10.2991/978-94-6463-858-5_6How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Jaina Patel
AU  - Bhumika Prajapati
PY  - 2025
DA  - 2025/11/04
TI  - Event-Trace: AI-Driven Law Section Predictions from Case Narratives
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 53
EP  - 64
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_6
DO  - 10.2991/978-94-6463-858-5_6
ID  - Patel2025
ER  -