# Performance in the Courtroom: Automated Processing and Visualization of Appeal Court Decisions in France

arXiv: https://arxiv.org/abs/2006.06251 · HTML: https://mlsanity.com/paper/2006.06251/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2020.

## Built on by (most cited first, 6 of 6)

- [Unlocking Practical Applications in Legal Domain: Evaluation of GPT for Zero-Shot Semantic Annotation of Legal Texts](https://mlsanity.com/paper/2305.04417/) (2023)
- [Factoring Statutory Reasoning as Language Understanding Challenges](https://mlsanity.com/paper/2105.07903/) (2021)
- [Lex Rosetta: Transfer of Predictive Models Across Languages, Jurisdictions, and Legal Domains](https://mlsanity.com/paper/2112.07882/) (2021)
- [The Factuality of Large Language Models in the Legal Domain](https://mlsanity.com/paper/2409.11798/) (2024)
- [NLP Research and Resources at DaSciM, Ecole Polytechnique](https://mlsanity.com/paper/2112.00566/) (2021)
- [What Are the Facts? Automated Extraction of Court-Established Facts from Criminal-Court Opinions](https://mlsanity.com/paper/2511.05320/) (2025)

## Abstract (arXiv)

Artificial Intelligence techniques are already popular and important in the legal domain. We extract legal indicators from judicial judgment to decrease the asymmetry of information of the legal system and the access-to-justice gap. We use NLP methods to extract interesting entities/data from judgments to construct networks of lawyers and judgments. We propose metrics to rank lawyers based on their experience, wins/loss ratio and their importance in the network of lawyers. We also perform community detection in the network of judgments and propose metrics to represent the difficulty of cases capitalising on communities features.