Towards an In-Depth Comprehension of Case Relevance for Better Legal Retrieval
arxiv(2024)
摘要
Legal retrieval techniques play an important role in preserving the fairness
and equality of the judicial system. As an annually well-known international
competition, COLIEE aims to advance the development of state-of-the-art
retrieval models for legal texts. This paper elaborates on the methodology
employed by the TQM team in COLIEE2024.Specifically, we explored various
lexical matching and semantic retrieval models, with a focus on enhancing the
understanding of case relevance. Additionally, we endeavor to integrate various
features using the learning-to-rank technique. Furthermore, fine heuristic
pre-processing and post-processing methods have been proposed to mitigate
irrelevant information. Consequently, our methodology achieved remarkable
performance in COLIEE2024, securing first place in Task 1 and third place in
Task 3. We anticipate that our proposed approach can contribute valuable
insights to the advancement of legal retrieval technology.
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