Legal Linking: Citation Resolution and Suggestion in Constitutional Law

Robert Shaffer, Stephen Mayhew


Abstract
This paper describes a dataset and baseline systems for linking paragraphs from court cases to clauses or amendments in the US Constitution. We implement a rule-based system, a linear model, and a neural architecture for matching pairs of paragraphs, taking training data from online databases in a distantly-supervised fashion. In experiments on a manually-annotated evaluation set, we find that our proposed neural system outperforms a rules-driven baseline. Qualitatively, this performance gap seems largest for abstract or indirect links between documents, which suggests that our system might be useful for answering political science and legal research questions or discovering novel links. We release the dataset along with the manually-annotated evaluation set to foster future work.
Anthology ID:
W19-2205
Volume:
Proceedings of the Natural Legal Language Processing Workshop 2019
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venues:
NAACL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
39–44
Language:
URL:
https://aclanthology.org/W19-2205
DOI:
10.18653/v1/W19-2205
Bibkey:
Cite (ACL):
Robert Shaffer and Stephen Mayhew. 2019. Legal Linking: Citation Resolution and Suggestion in Constitutional Law. In Proceedings of the Natural Legal Language Processing Workshop 2019, pages 39–44, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Legal Linking: Citation Resolution and Suggestion in Constitutional Law (Shaffer & Mayhew, 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-2205.pdf
Code
 mayhewsw/legal-linking