Abstract
Recent years have seen increasing attention on Legal Case Retrieval (LCR), a key task in the area of Legal AI that concerns the retrieval of cases from a large legal database of historical cases that are similar to a given query. This paper presents a survey of the major milestones made in LCR research, targeting researchers who are finding their way into the field and seek a brief account of the relevant datasets and the recent neural models and their performances.- Anthology ID:
- 2024.acl-long.350
- Volume:
- Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6472–6485
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2024.acl-long.350/
- DOI:
- 10.18653/v1/2024.acl-long.350
- Cite (ACL):
- Yi Feng, Chuanyi Li, and Vincent Ng. 2024. Legal Case Retrieval: A Survey of the State of the Art. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6472–6485, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Legal Case Retrieval: A Survey of the State of the Art (Feng et al., ACL 2024)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2024.acl-long.350.pdf