Case Vectors: Spatial Representations of the Law Using Document Embeddings

Law as Data, Santa Fe Institute Press, ed. M. Livermore and D. Rockmore, 2019(11)

25 Pages Posted: 29 Jun 2018 Last revised: 20 Apr 2020

See all articles by Elliott Ash

Elliott Ash

ETH Zürich

Daniel L. Chen

Directeur de Recherche, Centre National de la Recherche Scientifique, Toulouse School of Economics, Institute for Advanced Study in Toulouse, University of Toulouse Capitole, Toulouse, France

Date Written: June 28, 2018

Abstract

Recent work in natural language processing represents language objects (words and documents) as dense vectors that encode the relations between those objects. This paper explores the application of these methods to legal language, with the goal of understanding judicial reasoning and the relations between judges. In an application to federal appellate courts, we show that these vectors encode information that distinguishes courts, time, and legal topics. The vectors do not reveal spatial distinctions in terms of political party or law school attended, but they do highlight generational differences across judges. We conclude the paper by outlining a range of promising future applications of these methods.

Keywords: text data, judge rankings

Suggested Citation

Ash, Elliott and Chen, Daniel L., Case Vectors: Spatial Representations of the Law Using Document Embeddings (June 28, 2018). Law as Data, Santa Fe Institute Press, ed. M. Livermore and D. Rockmore, 2019(11), Available at SSRN: https://ssrn.com/abstract=3204926 or http://dx.doi.org/10.2139/ssrn.3204926

Elliott Ash (Contact Author)

ETH Zürich ( email )

Rämistrasse 101
ZUE F7
Zürich, 8092
Switzerland

Daniel L. Chen

Directeur de Recherche, Centre National de la Recherche Scientifique, Toulouse School of Economics, Institute for Advanced Study in Toulouse, University of Toulouse Capitole, Toulouse, France ( email )

Toulouse School of Economics
1, Esplanade de l'Université
Toulouse, 31080
France

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