Towards an In-Depth Comprehension of Case Relevance for Better Legal Retrieval
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866910393717227520 |
|---|---|
| author | Li, Haitao Chen, You Ge, Zhekai Ai, Qingyao Liu, Yiqun Zhou, Quan Huo, Shuai |
| author_facet | Li, Haitao Chen, You Ge, Zhekai Ai, Qingyao Liu, Yiqun Zhou, Quan Huo, Shuai |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_00947 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards an In-Depth Comprehension of Case Relevance for Better Legal Retrieval Li, Haitao Chen, You Ge, Zhekai Ai, Qingyao Liu, Yiqun Zhou, Quan Huo, Shuai Information 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. |
| title | Towards an In-Depth Comprehension of Case Relevance for Better Legal Retrieval |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2404.00947 |