Automated fact-value distinction in court opinions

Yu Cao, 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: 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. (Am Polit Sci Rev 97(2):311–331, 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: Facts versus law; Law and machine learning; Law and NLP; Text data (search for similar items in EconPapers)
Date: 2020-12
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Published in European Journal of Law and Economics, 2020, 50 (3), pp.451-467. ⟨10.1007/s10657-020-09645-7⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03174376

DOI: 10.1007/s10657-020-09645-7

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