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Mapping the Geometry of Law using Document Embeddings

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  • Ash, Elliott
  • Chen, Daniel L.

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.

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  • Ash, Elliott & Chen, Daniel L., 2018. "Mapping the Geometry of Law using Document Embeddings," TSE Working Papers 18-935, Toulouse School of Economics (TSE).
    • Handle: RePEc:tse:wpaper:32764
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    Cited by:

    1. Ramos Maqueda,Manuel & Chen,Daniel Li, 2021. "The Role of Justice in Development : The Data Revolution," Policy Research Working Paper Series 9720, The World Bank.
    2. Hausladen, Carina I. & Schubert, Marcel H. & Ash, Elliott, 2020. "Text classification of ideological direction in judicial opinions," International Review of Law and Economics, Elsevier, vol. 62(C).

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