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CaseLink: Inductive Graph Learning for Legal Case Retrieval (2403.17780v3)

Published 26 Mar 2024 in cs.IR

Abstract: In case law, the precedents are the relevant cases that are used to support the decisions made by the judges and the opinions of lawyers towards a given case. This relevance is referred to as the case-to-case reference relation. To efficiently find relevant cases from a large case pool, retrieval tools are widely used by legal practitioners. Existing legal case retrieval models mainly work by comparing the text representations of individual cases. Although they obtain a decent retrieval accuracy, the intrinsic case connectivity relationships among cases have not been well exploited for case encoding, therefore limiting the further improvement of retrieval performance. In a case pool, there are three types of case connectivity relationships: the case reference relationship, the case semantic relationship, and the case legal charge relationship. Due to the inductive manner in the task of legal case retrieval, using case reference as input is not applicable for testing. Thus, in this paper, a CaseLink model based on inductive graph learning is proposed to utilise the intrinsic case connectivity for legal case retrieval, a novel Global Case Graph is incorporated to represent both the case semantic relationship and the case legal charge relationship. A novel contrastive objective with a regularisation on the degree of case nodes is proposed to leverage the information carried by the case reference relationship to optimise the model. Extensive experiments have been conducted on two benchmark datasets, which demonstrate the state-of-the-art performance of CaseLink. The code has been released on https://github.com/yanran-tang/CaseLink.

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Authors (5)
  1. Yanran Tang (6 papers)
  2. Ruihong Qiu (26 papers)
  3. Hongzhi Yin (211 papers)
  4. Xue Li (124 papers)
  5. Zi Huang (126 papers)
Citations (1)
View on Semantic Scholar

Summary

  • The paper leverages a novel inductive graph learning approach using a Global Case Graph to capture critical legal case relationships.
  • It employs graph neural networks with a contrastive objective and regularization mechanism to map intricate case connections.
  • Empirical evaluations on COLIEE datasets show that CaseLink outperforms state-of-the-art models in retrieval accuracy and reliability.

Leveraging Inductive Graph Learning for Enhanced Legal Case Retrieval: A Close Examination of CaseLink

Introduction to Legal Case Retrieval Challenges

Legal Case Retrieval (LCR) has long been a challenging task within the information retrieval domain, owing to its critical role in the legal sector. Retrieval models play an essential part in aiding legal practitioners by efficiently navigating vast legal case databases to find relevant precedents. Traditional legal case retrieval models that rely on text representations provide decent accuracy but do not sufficiently capture the complex connectivity relationships ingrained among cases. This research introduces a novel approach that significantly enhances legal case retrieval by incorporating inductive graph learning.

The Genesis of CaseLink

The primary insight leading to the development of the CaseLink model is the recognition of intrinsic case connectivity relationships within legal databases. Traditional models fail to leverage three pivotal types of connectivity: case references, semantic relationships, and legal charge connections. CaseLink transforms these insights into a structured model that encapsulates the richness of legal case relationships. It thrives on a novel Global Case Graph (GCG) that captures semantic and legal charge relationships, coupled with an innovative contrastive objective function fortified with a regularisation mechanism focused on the degree of case nodes.

Methodology Explained

The underpinning structure of CaseLink involves creating a Global Case Graph (GCG), which serves as the bedrock for capturing case-to-case and case-to-charge relationships. The model employs graph neural networks for representation learning within this graph, ensuring that the intricate web of case connectivity is mapped effectively. This is coupled with a carefully formulated objective function that incorporates contrastive learning, directing the model to discern relevant cases efficiently.

Analytical Insights from Experiments

Emprirical evaluation on benchmark datasets, namely COLIEE2022 and COLIEE2023, reveals that CaseLink outperforms state-of-the-art performance across multiple metrics. This empirical validation underscores the efficacy of leveraging case connectivity relationships, significantly enhancing legal case retrieval's accuracy and reliability.

Theoretical and Practical Implications

From a theoretical standpoint, CaseLink's introduction of inductive graph learning to legal case retrieval marks a significant leap forward. It underscores the importance of leveraging the relational structure among cases beyond mere text representation. Practically, CaseLink offers a robust tool for legal practitioners, significantly reducing the time and effort required to identify relevant precedents amidst vast legal databases.

Future Trajectories in AI and Legal Informatics

The advent of CaseLink opens new avenues in the integration of graph learning principles within legal informatics. It paves the way for the development of more sophisticated models that could further unravel the complex network of legal precedents, potentially incorporating more nuanced legal concepts and relationships. Future research might delve into dynamic graph structures that evolve with new cases and legal judgments, offering even more refined retrieval capabilities.

Conclusion

In conclusion, CaseLink stands as a pioneering model that marries inductive graph learning with legal case retrieval. Its design and empirical success herald a new direction in legal informatics, leveraging connectivity relationships to enhance retrieval performance significantly. CaseLink not only sets a new benchmark for legal case retrieval models but also invites further exploration into the synergy between graph learning and legal precedent analysis.

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