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Automated fact-value distinction in court opinions

Author

Listed:
  • Yu Cao
  • Elliott Ash
  • Daniel L. Chen

    (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - Comue de Toulouse - Communauté d'universités et établissements 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

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.

Suggested Citation

  • Yu Cao & Elliott Ash & Daniel L. Chen, 2020. "Automated fact-value distinction in court opinions," Post-Print hal-03174376, HAL.
    • Handle: RePEc:hal:journl:hal-03174376
    DOI: 10.1007/s10657-020-09645-7
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    Cited by:

    1. Alain Marciano & Antonio Nicita & Giovanni Battista Ramello, 2020. "Puzzles in the big data revolution: an introduction," European Journal of Law and Economics, Springer, vol. 50(3), pages 339-344, December.

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    JEL classification:

    • K40 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - General

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