Automated Refugee Case Analysis: An NLP Pipeline for Supporting Legal Practitioners

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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 in 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 provide meaningful information in legal work, we propose to extend existing models and retrieve a total of 19 useful 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 noncontextual 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 above 90% on five of the targeted categories and over 80% on four further categories.
Original languageEnglish
Title of host publicationFindings of the Association for Computational Linguistics: ACL 2023
PublisherAssociation for Computational Linguistics (ACL)
Number of pages13
ISBN (Electronic)9781959429623
Publication statusPublished - 9 Jul 2023
Event61st Annual Meeting of the Association for Computational Linguistics - Toronto, Canada
Duration: 9 Jul 202314 Jul 2023
Conference number: 61


Conference61st Annual Meeting of the Association for Computational Linguistics
Abbreviated titleACL 2023
Internet address


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