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Theses

Méthodes d'apprentissage automatique pour l'analyse de corpus jurisprudentiels

Abstract : Judicial decisions contain deterministic information (whose content is recurrent from one decision to another) and random information (probabilistic). Both types of information come into play in a judge's decision-making process. The former can reinforce the decision insofar as deterministic information is a recurring and well-known element of case law (ie past business results). The latter, which are related to rare or exceptional characters, can make decision-making difficult, since they can modify the case law. The purpose of this thesis is to propose a deep learning model that would highlight these two types of information and study their impact (contribution) in the judge’s decision-making process. The objective is to analyze similar decisions in order to highlight random and deterministic information in a body of decisions and quantify their importance in the judgment process.
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Submitted on : Monday, May 9, 2022 - 9:50:12 AM
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Charles Condevaux. Méthodes d'apprentissage automatique pour l'analyse de corpus jurisprudentiels. Intelligence artificielle [cs.AI]. Université de Nîmes, 2021. Français. ⟨NNT : 2021NIME0008⟩. ⟨tel-03662129⟩

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