# Predicting the Law Area and Decisions of French Supreme Court Cases

arXiv: https://arxiv.org/abs/1708.01681 · HTML: https://mlsanity.com/paper/1708.01681/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2017.

## Built on by (most cited first, 30 of 30)

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- [From Citations to Criticality: Predicting Legal Decision Influence in the Multilingual Swiss Jurisprudence](https://mlsanity.com/paper/2410.13460/) (2024)
- [The Judge Variable: Challenging Judge-Agnostic Legal Judgment Prediction](https://mlsanity.com/paper/2507.13732/) (2025)
- [FAIR: A Causal Framework for Accurately Inferring Judgments Reversals](https://mlsanity.com/paper/2306.11585/) (2023)
- [Predicting delays in Indian lower courts using AutoML and Decision Forests](https://mlsanity.com/paper/2307.16285/) (2023)
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- [Vichara: Appellate Judgment Prediction and Explanation for the Indian Judicial System](https://mlsanity.com/paper/2602.18346/) (2026)

## Abstract (arXiv)

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.