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.

Replaced by

Daniel L. Chen, Sandeep Bhupatiraju, and Kannan Venkataramanan, “Mapping the Geometry of Law Using Natural Language Processing”, European Journal of Empirical Legal Studies, vol. 1, n. 1, May 2024, pp. 49–68.