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.
Highlights
Judicial prediction is the ability to predict what a judge will decide on a given case
Is it possible to develop efficient predictive models to automatize such predictions? This question has long been driving several initiatives at the crossroads of Artificial Intelligence and Law—in particular, through the development of predictive models based on the alignment of computable features of the case that were available to the judge prior to the judgment, with computable features of the judge’s decision on the case
In this line of work, this paper presents a study towards the development of such predictive models taking advantage of Machine Learning and Natural Language Processing techniques
Summary
Judicial prediction is the ability to predict what a judge will decide on a given case. Neural networks enable very good performances to be achieved, we defend in this paper the use of compression machine learning models based on word representations such as TF-IDF with different variants corresponding to different weighting schemes These approaches are suited dealing with small- to medium-size annotated datasets. The methodology of judicial predictions depends on the ability of a model to predict the judge’s decision on a claim inherent to a given category—without knowing the precise localization of the statement of the judge’s decision inside a judgment In this context, extracting the result of a claim can be formulated as a task of binary text classification.
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