Towards an In-Depth Comprehension of Case Relevance for Better Legal Retrieval
| Autor: | Li, Haitao, Chen, You, Ge, Zhekai, Ai, Qingyao, Liu, Yiqun, Zhou, Quan, Huo, Shuai |
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| Rok vydání: | 2024 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: 16 pages |
| Databáze: | arXiv |
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