Volume 11 • Issue 2 • PP: 05–09 • 2026
IHLawRecommender: Deep Semantic Modelling for IPC Case Recommendation with Legal Domain Constraints
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