Mapping the Geometry of Law using Document Embeddings

Elliott Ash and Daniel Chen

No 18-935, TSE Working Papers from Toulouse School of Economics (TSE)

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.

Date: 2018-07
New Economics Papers: this item is included in nep-big and nep-law
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Working Paper: Mapping the Geometry of Law using Document Embeddings (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:tse:wpaper:32764

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