Volume 11 • Issue 2 • PP: 05–09 • 2026
IHLawRecommender: Deep Semantic Modelling for IPC Case Recommendation with Legal Domain Constraints
Open Access & Copyright
© 2026 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
View PDF Abstract
Efficient retrieval of relevant legal cases is critical for judicial decision-making, particularly for high-severity crimes where timely reference to precedents can influence outcomes. Our work presents IHLawRecommender, i.e., Intelligent Hybrid Law Recommender, a hybrid framework for recommending Indian Penal Code (IPC) cases based on textual descriptions provided by users. The system operates through a multi-stage workflow: first, case descriptions are normalized to remove inconsistencies and embedded into semantic vectors using a Bi-directional Long Short-Term Memory (BiLSTM) network. These embeddings are compared with the user query to measure semantic similarity. In parallel, an IPC-specific keyword map evaluates the relevance of each case, while legal aware filters distinguish between sexual and non-sexual violent crimes to ensure contextually appropriate recommendations. The outputs from these stages are integrated using a weighted payoff function that considers semantic similarity, keyword relevance, and crime severity to produce a ranked list of top-k cases. The system also provides interpretable visualizations, including heatmaps that illustrate correlations between similarity, keyword score, severity, and payoff. Evaluation on a curated IPC dataset demonstrates that IHLawRecommender consistently prioritizes legally critical cases, reduces irrelevant matches, and offers a practical, workflow-driven tool for legal professionals to efficiently navigate case law while maintaining adherence to judicial priorities.
Keywords
References
[1] D. Premasiri, O. Seneviratne, K. Thirunarayan, and I. Frommholz, “Survey on legal information extraction: current status and open challenges,” Knowledge and Information Systems, vol. 67, no. 2, pp. 345–378, 2025.
[2] A. Singh, A. Joshi, J. Jiang, and H.-Y. Paik, “A survey of classification tasks and approaches for legal contracts,” Artificial Intelligence Review, vol. 58, no. 1, pp. 1123– 1156, 2025.
[3] Y. Zhang, R. Wang, and Z. Li, “Large language models meet legal artificial intelligence: A survey,” Artificial Intelligence and Law, vol. 33, no. 1, pp. 1–32, 2025.
[4] I. Chalkidis, I. Androutsopoulos, and D. M. Katz, “Natural language processing for the legal domain: A survey,” ACM Computing Surveys, vol. 56, no. 3, pp. 1–45, 2024.
[5] M. J. Bommarito, D. M. Katz, and J. Zelner, “Large language models in law: Opportunities and challenges,” Artificial Intelligence Review, vol. 57, no. 4, pp. 987– 1015, 2024.
[6] I. Chalkidis, M. Fergadiotis, P. Malakasiotis, N. Aletras, and I. Androutsopoulos, “Lexglue: A benchmark dataset for legal language understanding,” in Proceedings of EMNLP, 2022, pp. 431–445.
[7] D. M. Katz, M. J. Bommarito, and J. Blackman, “A general approach for predicting the behavior of the supreme court of the united states,” PLOS ONE, vol. 16, no. 4, p. e0249707, 2021.
[8] M. Medvedeva, M. Vols, and M. Wieling, “Using machine learning to predict decisions of the European court of human rights,” Artificial Intelligence and Law, vol. 28, no. 2, pp. 237–266, 2020.
[9] N. Aletras, D. Tsarapatsanis, D. Preo，tiuc-Pietro, and V. Lampos, “Predicting judicial decisions of the European court of human rights: A natural language processing perspective,” PeerJ Computer Science, vol. 6, p. e278, 2020.
[10] D. M. Katz and M. J. Bommarito, “Measuring the complexity of the law,” in Proceedings of the IEEE International Conference on Artificial Intelligence and Law, 2020, pp. 1–10.
[11] H. Surden and A. Williams, “Application of artificial intelligence in justice systems: Current trends and future prospects,” AI and Ethics, vol. 4, no. 2, pp. 289–305, 2024.
[12] C. M. Greco and A. Tagarelli, “Transformer-based language models for artificial intelligence and law: A survey,” Artificial Intelligence and Law, vol. 31, no. 3, pp. 421–470, 2023.
[13] H. Zhong, Z. Guo, C. Tu, and C. Xiao, “Legal judgment prediction: A survey of methods and datasets,” IEEE Access, vol. 11, pp. 56 321–56 338, 2023.
[14] J. Cui, X. Shen, F. Nie, Z.Wang, and Y. Chen, “A survey on legal judgment prediction: Datasets, metrics, and methods,” IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 11, pp. 5234–5248, 2022.
[15] P. Kalamkar, J. Venugopalan, and V. Raghavan, “Benchmarks for indian legal natural language processing: A survey,” in Proceedings of the JSAI International Symposium on Artificial Intelligence, ser. LNCS, vol. 12892. Springer, 2021, pp. 156–170.
Cite This Article
Choose your preferred format
Publisher's Note
The statements, opinions, and data presented in this article are solely those of the author(s) and do not necessarily represent those of ASPG, the journal, or its editors. ASPG and the editors disclaim responsibility for any harm arising from the use of any ideas, methods, instructions, or products described in this article, to the fullest extent permitted by applicable law.