U-CREAT: Unsupervised Case Retrieval using Events extrAcTion

TL;DR

This paper introduces U-CREAT, an unsupervised method leveraging event extraction for legal case retrieval, demonstrating improved accuracy and speed across Indian and Canadian legal systems, and providing a new benchmark dataset.

Contribution

The paper presents U-CREAT, a novel unsupervised retrieval pipeline that enhances legal case retrieval by incorporating event extraction, outperforming traditional methods like BM25.

Findings

U-CREAT outperforms BM25 in accuracy.

The method significantly speeds up retrieval.

It generalizes across Indian and Canadian legal systems.

Abstract

The task of Prior Case Retrieval (PCR) in the legal domain is about automatically citing relevant (based on facts and precedence) prior legal cases in a given query case. To further promote research in PCR, in this paper, we propose a new large benchmark (in English) for the PCR task: IL-PCR (Indian Legal Prior Case Retrieval) corpus. Given the complex nature of case relevance and the long size of legal documents, BM25 remains a strong baseline for ranking the cited prior documents. In this work, we explore the role of events in legal case retrieval and propose an unsupervised retrieval method-based pipeline U-CREAT (Unsupervised Case Retrieval using Events Extraction). We find that the proposed unsupervised retrieval method significantly increases performance compared to BM25 and makes retrieval faster by a considerable margin, making it applicable to real-time case retrieval systems. Our proposed system is generic, we show that it generalizes across two different legal systems (Indian and Canadian), and it shows state-of-the-art performance on the benchmarks for both the legal systems (IL-PCR and COLIEE corpora).