Effective Representation Learning for Legal Case Retrieval

The School of EECS is hosting the following PhD Progress Review 1 Confirmation Seminar:

Effective Representation Learning for Legal Case Retrieval

Speaker: Yanran Tang
Host: Prof Helen Huang

Abstract: Legal case retrieval (LCR) is a specialised and indispensable retrieval task that focuses on retrieving relevant cases given a query case. For legal practitioners such as judges and lawyers, using retrieval tools is more efficient than manually finding relevant cases by looking into thousands of legal documents. The methods of LCR can be generally divided into two branches, statistical retrieval models that measure the term frequency similarity between cases and neural LCR models that encode the case into a representation to conduct nearest neighbour search. However, the legal domain-specific knowledge that can reveal the relevance among cases has not been well exploited in the existing LCR models. Thus, to further enhance the learning ability and retrieval accuracy of LCR models, three legal specific aspects are investigated and utilised to enhance the LCR accuracy: legal determining features, legal structural information, and legal connectivity relationships.

Bio: Yanran Tang is currently a PhD student at the School of Electrical Engineering and Computer Science, the University of Queensland. She holds an LLB and an LLM degrees. Her research interests include information retrieval and graph representation learning in legal domain.