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Found 4 papers, 3 papers with code
CaseLink: Inductive Graph Learning for Legal Case Retrieval
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.
PUMA: Efficient Continual Graph Learning with Graph Condensation
Our prior work, CaT is a replay-based framework with a balanced continual learning procedure, which designs a small yet effective memory bank for replaying data by condensing incoming graphs.
CaseGNN: Graph Neural Networks for Legal Case Retrieval with Text-Attributed Graphs
Previous neural legal case retrieval models mostly encode the unstructured raw text of case into a case representation, which causes the lack of important legal structural information in a case and leads to poor case representation; (2) Lengthy legal text limitation.
Prompt-based Effective Input Reformulation for Legal Case Retrieval
Although these straightforward methods have achieved improvement over conventional statistical methods in retrieval accuracy, two significant challenges are identified in this paper: (1) Legal feature alignment: the usage of the whole case text as the input will generally incorporate redundant and noisy information because, from the legal perspective, the determining factor of relevant cases is the alignment of key legal features instead of whole text matching; (2) Legal context preservation: furthermore, since the existing text encoding models usually have an input length limit shorter than the case, the whole case text needs to be truncated or divided into paragraphs, which leads to the loss of the global context of legal information.
