Abstract
The existing methods for predicting Easily Confused Charges (ECC) primarily rely on factual descriptions from legal cases. However, these approaches overlook some key information hidden in these descriptions, resulting in an inability to accurately differentiate between ECC. Legal domain knowledge graphs can showcase personal information and criminal processes in cases, but they primarily focus on entities in cases of insolation while ignoring the logical relationships between these entities. Different relationships often lead to distinct charges. To address these problems, this paper proposes a charge prediction model that integrates a Criminal Behavior Knowledge Graph (CBKG), called Charge Prediction Knowledge Graph (CP-KG). Firstly, we defined a diverse range of legal entities and relationships based on the characteristics of ECC. We conducted fine-grained annotation on key elements and logical relationships in the factual descriptions. Subsequently, we matched the descriptions with the CBKG to extract the key elements, which were then encoded by Text Convolutional Neural Network (TextCNN). Additionally, we extracted case subgraphs containing sequential behaviors from the CBKG based on the factual descriptions and encoded them using a Graph Attention Network (GAT). Finally, we concatenated these representations of key elements, case subgraphs, and factual descriptions, collectively used for predicting the charges of the defendant. To evaluate the CP-KG, we conducted experiments on two charge prediction datasets consisting of real legal cases. The experimental results demonstrate that the CP-KG achieves scores of 99.10% and 90.23% in the Macro-F1 respectively. Compared to the baseline methods, the CP-KG shows significant improvements with 25.79% and 13.82% respectively.
S. Gao and R. Sa—Equal Contribution.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Jiang, X., Ye, H., Luo, Z., Chao, W., Ma, W.: Interpretable rationale augmented charge prediction system. In: Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations, pp. 146–151 (2018)
Zhong, H., Guo, Z., Tu, C., Xiao, C., Liu, Z., Sun, M.: Legal judgment prediction via topological learning. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3540–3549 (2018)
Yang, W., Jia, W., Zhou, X., Luo, Y.: Legal judgment prediction via multi-perspective bi-feedback network. In: Twenty-Eighth International Joint Conference on Artificial Intelligence (2019)
Zhao, J., Guan, Z., Xu, C., Zhao, W., Chen, E.: Charge prediction by constitutive elements matching of crimes. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, vol. 22(23–29), pp. 4517–4523 (2022)
Chen, S., Wang, P., Fang, W., Deng, X., Zhang, F.: Learning to predict charges for judgment with legal graph. In: Artificial Neural Networks and Machine Learning, pp. 240–252 (2019)
Chen, J.X., Huang, Y.J., Cao, G.J., Yang, F., Li, C., Ma, Z.B.: Research and implementation of judicial case visualization based on knowledge graph. J. Hubei Univ. Technol. 34(05), 72–77 (2019)
Chen, W.Z.: Research on Legal Text Representation Method Fused with Knowledge Graph. GuiZhou University (2020)
Guo, J.: Research and Implementation of Auxiliary Judgment Technology Based on Affair Graph. Beijing University of Posts and Telecommunications (2021)
Chen, Y.G.: Research on Entity Relationship Extraction Algorithm for Legal Documents. Dalian University of Technology (2021)
Hong, W.X., Hu, Z.Q., Weng, Y., Zhang, H., Wang, Z., Guo, Z.X.: Automatic construction of case knowledge graph for judicial cases. J. Chin. Inf. Process. 34(01), 34–44 (2020)
Wang, Z.Z., et al.: Sentencing prediction based on multi-view knowledge graph embedding. Pattern Recogn. Artif. Intell. 34(07), 655–665 (2021)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)
Xiao, C., Zhong, H., Guo, Z., et al.: CAIL2018: a large-scale legal dataset for judgment prediction. arXiv preprint (2018)
Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint (2014)
Yue, L., Liu, Q., Jin, B., et al.: NeurJudge: a circumstance-aware neural framework for legal judgment prediction. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 973–982 (2021)
Ma, L., et al.: Legal judgment prediction with multi-stage case representation learning in the real court setting. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 993–1002 (2021)
Lyu, Y., et al.: Improving legal judgment prediction through reinforced criminal element extraction. Inf. Process. Manage. 59(1), 102780 (2022)
Dong, Q., Niu, S.: Legal judgment prediction via relational learning. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 983–992 (2021)
Feng, Y., Li, C., Ng, V.: Legal judgment prediction via event extraction with constraints. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, pp. 648–664 (2022)
Acknowledgments
This paper was supported by the National Natural Science Foundation of China (12204062, 61806103, 61562068), National Natural Science Foundation of Inner Mongolia, China (2022LHMS06001), Basic Scientific Research Business Project of Inner Mongolia Normal University (2022JBQN106, 2022JBQN111).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gao, S. et al. (2024). How Legal Knowledge Graph Can Help Predict Charges for Legal Text. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14452. Springer, Singapore. https://doi.org/10.1007/978-981-99-8076-5_30
Download citation
DOI: https://doi.org/10.1007/978-981-99-8076-5_30
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8075-8
Online ISBN: 978-981-99-8076-5
eBook Packages: Computer ScienceComputer Science (R0)