Automated Fact-Value Distinction in Court Opinions

16 Pages Posted: 25 Jun 2018 Last revised: 26 Aug 2019

See all articles by Yu Cao

Yu Cao

Department of Linguistics, Rutgers

Elliott Ash

ETH Zürich

Daniel L. Chen

Directeur de Recherche, Centre National de la Recherche Scientifique, Toulouse School of Economics, Institute for Advanced Study in Toulouse, University of Toulouse Capitole, Toulouse, France

Date Written: August 2019

Abstract

This paper studies the problem of automated classification of fact statements and value statements in written judicial decisions. We compare a range of methods and demonstrate that the linguistic features of sentences and paragraphs can be used to successfully classify them along this dimension. The Wordscores method by Laver et al. (2003) performs best in held out data. In an application, we show that the value segments of opinions are more informative than fact segments of the ideological direction of U.S. Circuit Court opinions.

Keywords: Court opinions, Fact, Value, Text classification, Natural language processing

Suggested Citation

Cao, Yu and Ash, Elliott and Chen, Daniel L., Automated Fact-Value Distinction in Court Opinions (August 2019). Available at SSRN: https://ssrn.com/abstract=3202438 or http://dx.doi.org/10.2139/ssrn.3202438

Yu Cao (Contact Author)

Department of Linguistics, Rutgers ( email )

18 Seminary Place
New Brunswick, NJ 08901
United States

Elliott Ash

ETH Zürich ( email )

Rämistrasse 101
ZUE F7
Zürich, 8092
Switzerland

Daniel L. Chen

Directeur de Recherche, Centre National de la Recherche Scientifique, Toulouse School of Economics, Institute for Advanced Study in Toulouse, University of Toulouse Capitole, Toulouse, France ( email )

Toulouse School of Economics
1, Esplanade de l'Université
Toulouse, 31080
France

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