Uncovering connections: a reference network approach to statute law retrieval

Abstract

The increasing volume and complexity of statute law data have led to a growing demand for efficient and effective retrieval methods. This paper presents a novel approach to statute law retrieval that utilizes reference networks to uncover connections between laws. By representing law articles as a network of references, our method allows users to quickly identify relevant direct and indirect articles. The key point is that the reference network can encode both internal and external legal relations, helping to integrate both the local and the long-range dependencies into the final retrieval model. The proposed approach is evaluated on several statute law corpora and shows that it performs better existing methods on the same tasks. In addition, our finding is that internal references help enhance the accuracy significantly while external links are also important. Our empirical study also suggests the optimal range of local window size to achieve a balance between retrieval accuracy and noise. Our approach can also contribute to the development of AI-assisted legal research tools, making it easier for legal practitioners to find relevant laws and precedents. Furthermore, by uncovering hidden connections between laws, our method can help identify inconsistencies and gaps in the legal system, ultimately improving its effectiveness and reliability.