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

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...

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Bibliographic Details
Published inStats (Basel, Switzerland) Vol. 3; no. 4; pp. 427 - 443
Main Authors Tagny-Ngompé, Gildas, Mussard, Stéphane, Zambrano, Guillaume, Harispe, Sébastien, Montmain, Jacky
Format Journal Article
LanguageEnglish
Published MDPI 01.12.2020
MDPI AG
Subjects
Applications
Computer Science
court decisions
Document and Text Processing
Gini-PLS
judge opinion identification
Mathematics
Statistics
text classification
Gini-PLS
text classification
judge opinion identification
court decisions
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Summary: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.
ISSN:2571-905X
2571-905X
DOI:10.3390/stats3040027