Abstract
In this paper, we introduce an end-to-end pipeline for retrieving, processing, and extracting targeted information from legal cases. We investigate an under-studied legal domain with a case study on refugee law Canada. Searching case law for past similar cases is a key part of legal work for both lawyers and judges, the potential end-users of our prototype. While traditional named-entity recognition labels such as dates are meaningful information in law, we propose to extend existing models and retrieve a total of 19 categories of items from refugee cases.After creating a novel data set of cases, we perform information extraction based on state-of-the-art neural named-entity recognition (NER). We test different architectures including two transformer models, using contextual and non-contextual embeddings, and compare general purpose versus domain-specific pre-training. The results demonstrate that models pre-trained on legal data perform best despite their smaller size, suggesting that domain-matching had a larger effect than network architecture. We achieve a F1- score superior to 90% on five of the targeted categories and superior to 80% on an additional 4 categories.- Anthology ID:
- 2023.findings-acl.187
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2992–3005
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.187
- DOI:
- 10.18653/v1/2023.findings-acl.187
- Cite (ACL):
- Claire Barale, Michael Rovatsos, and Nehal Bhuta. 2023. Automated Refugee Case Analysis: A NLP Pipeline for Supporting Legal Practitioners. In Findings of the Association for Computational Linguistics: ACL 2023, pages 2992–3005, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Automated Refugee Case Analysis: A NLP Pipeline for Supporting Legal Practitioners (Barale et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2023.findings-acl.187.pdf