Towards an In-Depth Comprehension of Case Relevance for Better Legal Retrieval
<|reference_start|>Towards an In-Depth Comprehension of Case Relevance for Better Legal Retrieval: 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.<|reference_end|>
@article{li2024towards,
    title={Towards an In-Depth Comprehension of Case Relevance for Better Legal
  Retrieval},
    author={Haitao Li, You Chen, Zhekai Ge, Qingyao Ai, Yiqun Liu, Quan Zhou,
  Shuai Huo},
    journal={arXiv preprint arXiv:2404.00947},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00947},
    primaryClass={cs.IR}
}