Publication Type

Journal Article

Version

publishedVersion

Publication Date

4-2024

Abstract

We propose and evaluate an automated pipeline for discovering significant topics from legal decision texts by passing features synthesized with topic models through penalized regressions and post-selection significance tests. The method identifies case topics significantly correlated with outcomes, topic-word distributions which can be manually interpreted to gain insights about significant topics, and case-topic weights which can be used to identify representative cases for each topic. We demonstrate the method on a new dataset of domain name disputes and a canonical dataset of European Court of Human Rights violation cases. Topic models based on latent semantic analysis as well as language model embeddings are evaluated. We show that topics derived by the pipeline are consistent with legal doctrines in both areas and can be useful in other related legal analysis tasks. This article is part of the theme issue 'A complexity science approach to law and governance'.

Keywords

domain name disputes, European Court of Human Rights, legal language processing, text-as-data, topic models

Discipline

Courts | Legal Studies | Numerical Analysis and Scientific Computing

Research Areas

Innovation, Technology and the Law

Publication

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences

Volume

382

Issue

2270

First Page

1

Last Page

21

ISSN

1364-503X

Identifier

10.1098/rsta.2023.0147

Publisher

The Royal Society

Copyright Owner and License

Authors-CC-BY

Creative Commons License

Creative Commons Attribution 3.0 License
This work is licensed under a Creative Commons Attribution 3.0 License.

Additional URL

https://doi.org/10.1098/rsta.2023.0147

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