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Case Vectors: Spatial Representations of the Law Using Document Embeddings

Author

Listed:
  • Elliott Ash

    (University of Warwick [Coventry])

  • 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)

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

  • Elliott Ash & Daniel L. Chen, 2019. "Case Vectors: Spatial Representations of the Law Using Document Embeddings," Post-Print hal-03161822, HAL.
    • Handle: RePEc:hal:journl:hal-03161822
    DOI: 10.2139/ssrn.3204926
    Note: View the original document on HAL open archive server: https://hal.science/hal-03161822
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    Cited by:

    1. Elliott Ash & Daniel L. Chen & Sergio Galletta, 2022. "Measuring Judicial Sentiment: Methods and Application to US Circuit Courts," Economica, London School of Economics and Political Science, vol. 89(354), pages 362-376, April.
    2. 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.

    More about this item

    Keywords

    Text data; Judge rankings;

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