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        <title>Legal Linking: Citation Resolution and Suggestion in Constitutional Law</title>
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            <title>Proceedings of the Natural Legal Language Processing Workshop 2019</title>
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            <namePart type="given">Nikolaos</namePart>
            <namePart type="family">Aletras</namePart>
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        <name type="personal">
            <namePart type="given">Elliott</namePart>
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        <name type="personal">
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    <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.</abstract>
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%0 Conference Proceedings
%T Legal Linking: Citation Resolution and Suggestion in Constitutional Law
%A Shaffer, Robert
%A Mayhew, Stephen
%Y Aletras, Nikolaos
%Y Ash, Elliott
%Y Barrett, Leslie
%Y Chen, Daniel
%Y Meyers, Adam
%Y Preotiuc-Pietro, Daniel
%Y Rosenberg, David
%Y Stent, Amanda
%S Proceedings of the Natural Legal Language Processing Workshop 2019
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F shaffer-mayhew-2019-legal
%X 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.
%R 10.18653/v1/W19-2205
%U https://aclanthology.org/W19-2205/
%U https://doi.org/10.18653/v1/W19-2205
%P 39-44

Markdown (Informal)

[Legal Linking: Citation Resolution and Suggestion in Constitutional Law](https://aclanthology.org/W19-2205/) (Shaffer & Mayhew, NAACL 2019)

• Legal Linking: Citation Resolution and Suggestion in Constitutional Law (Shaffer & Mayhew, NAACL 2019)

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.