Case Vectors: Spatial Representations of the Law Using Document Embeddings

Elliott Ash and Daniel L. Chen
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Daniel L. Chen: TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, CNRS - Centre National de la Recherche Scientifique

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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 (search for similar items in EconPapers)
Date: 2019
Note: View the original document on HAL open archive server: https://hal.science/hal-03161822v1
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Citations: View citations in EconPapers (2)

Published in Law as Data, 2019, 11, ⟨10.2139/ssrn.3204926⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03161822

DOI: 10.2139/ssrn.3204926

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