Predicting the Law Area and Decisions of French Supreme Court Cases

Octavia-Maria Şulea · Marcos Zampieri · Mihaela Vela · Josef van Genabith

114 citations0/0 files storedInternational conference Recent advances in natural language processing

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Artificial Intelligence in Law Text and Document Classification Technologies Computational and Text Analysis Methods

Abstract

In this paper, we investigate the application of text classification methods to predict the law area and the decision of cases judged by the French Supreme Court.We also investigate the influence of the time period in which a ruling was made over the textual form of the case description and the extent to which it is necessary to mask the judge's motivation for a ruling to emulate a real-world test scenario.We report results of 96% f1 score in predicting a case ruling, 90% f1 score in predicting the law area of a case, and 75.9% f1 score in estimating the time span when a ruling has been issued using a linear Support Vector Machine (SVM) classifier trained on lexical features.

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References

2016Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective 2017A general approach for predicting the behavior of the Supreme Court of the United States 2009Argumentation mining 2012Predicting Dark Triad Personality Traits from Twitter Usage and a Linguistic Analysis of Tweets 2007Extractive summarisation of legal texts 2013Plagiarism Meets Paraphrasing: Insights for the Next Generation in Automatic Plagiarism Detection 2014Predicting the Behavior of the Supreme Court of the United States: A General Approach

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