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Identification of Judicial Outcomes in Judgments: A Generalized Gini-PLS Approach

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  • Gildas Tagny-Ngompé

    (EuroMov Digital Health in Motion, University of Montpellier, IMT Mines Ales, 30100 Ales, France)

  • Stéphane Mussard

    (CHROME, University of Nîmes, Avenue du Dr. Georges Salan, 30000 Nimes, France)

  • Guillaume Zambrano

    (CHROME, University of Nîmes, Avenue du Dr. Georges Salan, 30000 Nimes, France)

  • Sébastien Harispe

    (EuroMov Digital Health in Motion, University of Montpellier, IMT Mines Ales, 30100 Ales, France)

  • Jacky Montmain

    (EuroMov Digital Health in Motion, University of Montpellier, IMT Mines Ales, 30100 Ales, France)

Abstract

This paper presents and compares several text classification models that can be used to extract the outcome of a judgment from justice decisions, i.e., legal documents summarizing the different rulings made by a judge. Such models can be used to gather important statistics about cases, e.g., success rate based on specific characteristics of cases’ parties or jurisdiction, and are therefore important for the development of Judicial prediction not to mention the study of Law enforcement in general. We propose in particular the generalized Gini-PLS which better considers the information in the distribution tails while attenuating, as in the simple Gini-PLS, the influence exerted by outliers. Modeling the studied task as a supervised binary classification, we also introduce the LOGIT-Gini-PLS suited to the explanation of a binary target variable. In addition, various technical aspects regarding the evaluated text classification approaches which consists of combinations of representations of judgments and classification algorithms are studied using an annotated corpora of French justice decisions.

Suggested Citation

  • Gildas Tagny-Ngompé & Stéphane Mussard & Guillaume Zambrano & Sébastien Harispe & Jacky Montmain, 2020. "Identification of Judicial Outcomes in Judgments: A Generalized Gini-PLS Approach," Stats, MDPI, vol. 3(4), pages 1-17, September.
    • Handle: RePEc:gam:jstats:v:3:y:2020:i:4:p:27-443:d:420267
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    References listed on IDEAS

    as
    1. Yushu Liu & William Rayens, 2007. "PLS and dimension reduction for classification," Computational Statistics, Springer, vol. 22(2), pages 189-208, July.
    2. E. Schechtman & S. Yitzhaki, 2003. "A Family of Correlation Coefficients Based on the Extended Gini Index," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 1(2), pages 129-146, August.
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