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arXiv · 2404.00947

Towards an In-Depth Comprehension of Case Relevance for Better Legal Retrieval

Haitao Li·You Chen·Zhekai Ge·Qingyao Ai·Yiqun Liu·Quan Zhou·Shuai Huo
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Abstract

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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Haitao Li, You Chen, Zhekai Ge, Qingyao Ai, Yiqun Liu, Quan Zhou, Shuai Huo. 2024-04-01. Towards an In-Depth Comprehension of Case Relevance for Better Legal Retrieval. https://arxiv.org/abs/2404.00947

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