Mapping the Geometry of Law Using Document Embeddings

26 Pages Posted: 7 Jan 2019

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: July 2, 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.

Suggested Citation

Ash, Elliott and Chen, Daniel L., Mapping the Geometry of Law Using Document Embeddings (July 2, 2018). Available at SSRN: https://ssrn.com/abstract=3305761 or http://dx.doi.org/10.2139/ssrn.3305761

Elliott Ash

ETH Zürich ( email )

Rämistrasse 101
ZUE F7
Zürich, 8092
Switzerland

Daniel L. Chen (Contact Author)

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

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
39
Abstract Views
403
PlumX Metrics