Abstract
Similar case matching (SCM) is an essential task in legal intelligence, as it aids in making judicial decisions by identifying similar cases from past legal documents. However, retrieving relevant cases from large volumes of legal documents poses significant challenges. Existing approaches often treat SCM as a text classification task, but they typically overlook the crucial event information that can impact verdicts and case similarity, like legal events. Additionally, manually labeling events in SCM datasets can be time-consuming. To tackle these issues, we propose a novel Event-Context Detection Model called ECDM, which not only detects events but also provides event context information to improve downstream task efficiency. Besides, ECDM leverages a pre-trained event detection model instead of manual event labeling for the target SCM dataset in this study. We conduct extensive experiments to evaluate the performance of our model, and the results show that our method improves the accuracy by an average of 10% compared to the baselines. The experiment indicates that ECDM effectively leverages event-context knowledge to enhance SCM performance and holds promise for application in other downstream subtasks of legal intelligence.
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Dan, J., Xu, L., Hu, W., Wang, Y., Wang, Y. (2023). Enhancing Similar Case Matching with Event-Context Detection in Legal Intelligence. In: Liu, F., Duan, N., Xu, Q., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2023. Lecture Notes in Computer Science(), vol 14303. Springer, Cham. https://doi.org/10.1007/978-3-031-44696-2_43
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