U-CREAT: Unsupervised Case Retrieval using Events extrAcTion
Abhinav Joshi Akshat Sharma Sai Kiran Tanikella Ashutosh Modi
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).