A Retrieval-Augmented Contrastive Framework for Legal Case Retrieval Based on Event Information
Keywords: Similar Case Retrieval, Legal Case, Event Extraction
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Abstract: Similar case retrieval is a crucial aspect of the legal retrieval field, significantly contributing to LegalAI systems. This task aims to retrieve cases that are highly relevant to the query case, thereby enhancing the efficiency of legal practitioners. Recent methods have leveraged the rich semantic knowledge of pre-trained models, greatly improving retrieval performance. However, these methods often overlook key legal elements within the complex language structures of case texts, such as legal event information that can impact case outcomes and judgments. This oversight results in the underutilization of critical case information. To address this issue, we proposed RAEvent, a similar case retrieval contrastive framework augmented by legal event information. This framework utilizes an enhanced case event information database to provide auxiliary information for case retrieval and employs contrastive learning techniques to better extract similar features in cases. In comparison to a range of baseline approaches, the results of our experiments highlight the efficacy of our framework. Moreover, our research provides fresh perspectives and makes a valuable contribution to the ongoing studies in similar case retrieval tasks.
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Submission Number: 128
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