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Automated fact-value distinction in court opinions

Author

Listed:
  • Yu Cao

    (Rutgers)

  • Elliott Ash

    (ETH Zurich)

  • Daniel L. Chen

    (Toulouse School of Economics)

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," European Journal of Law and Economics, Springer, vol. 50(3), pages 451-467, December.
    • Handle: RePEc:kap:ejlwec:v:50:y:2020:i:3:d:10.1007_s10657-020-09645-7
    DOI: 10.1007/s10657-020-09645-7
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    References listed on IDEAS

    as
    1. Laver, Michael & Benoit, Kenneth & Garry, John, 2003. "Extracting Policy Positions from Political Texts Using Words as Data," American Political Science Review, Cambridge University Press, vol. 97(2), pages 311-331, May.
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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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    More about this item

    Keywords

    K40; Facts versus law; Law and machine learning; Law and NLP; Text data;
    All these keywords.

    JEL classification:

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

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