[Paper Review] Performance in the Courtroom: Automated Processing and Visualization of Appeal Court Decisions in France

Boniol Paul, P George+4 more|arXiv (Cornell University)|Jun 11, 2020

Artificial Intelligence in Law22 references5 citations

TL;DR

This paper proposes an NLP and network analysis framework to automate the processing and visualization of French appeal court decisions, extracting legal entities and constructing networks of lawyers and cases to rank legal performance and assess case difficulty. By applying Named Entity Recognition, graph mining, and community detection, the study enables data-driven insights into lawyer effectiveness and case complexity, aiming to reduce the access-to-justice gap through improved transparency for laypersons.

ABSTRACT

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.

Motivation & Objective

• To reduce the asymmetry of information in the legal market by extracting actionable insights from French judicial decisions.
• To improve laypersons' access to justice by providing data-driven tools for selecting experienced and effective lawyers.
• To develop metrics for measuring case difficulty based on legal community structure and win/loss patterns.
• To construct networks of lawyers and judgments to visualize legal performance and systemic patterns.
• To support the design of user-friendly legal interfaces that guide non-experts through complex legal procedures.

Proposed method

• Named Entity Recognition (NER) models are applied to extract lawyers and legal entities from 40,000 French appeal court judgments.
• Two networks are constructed: an opposing network (appellant vs. appellee) and a collaborative network (co-counseling lawyers) to assess lawyer performance.
• Weighted directed PageRank is used to rank lawyers, with edge weights reflecting win counts and role (appellant vs. appellee), emphasizing high-value wins.
• A case similarity network is built using shared citations of legal articles, with edges formed when two cases cite at least k common articles.
• Community detection algorithms identify clusters of cases with thematic similarity, based on dense internal connections and sparse inter-community links.
• Case difficulty is estimated using the win/loss ratio within detected communities, with high-losing-rate communities indicating greater difficulty.

Experimental results

Research questions

• RQ1 How can NLP techniques be used to extract meaningful legal entities and performance indicators from French judicial decisions?
• RQ2 What metrics can effectively rank lawyers based on their experience, win/loss ratio, and network centrality?
• RQ3 How can network analysis of judgments reveal communities of similar legal issues and inform case difficulty assessment?
• RQ4 To what extent can win/loss rates within legal communities serve as a proxy for case difficulty?
• RQ5 How can automated processing of judgments improve access to justice for laypersons?

Key findings

• Lawyers ranked highly by PageRank are those who win frequently as appellants against experienced opponents, indicating higher strategic effectiveness.
• Lawyer 350 is preferred over lawyer 387 for appellants, despite a lower win/loss ratio, because 350 has more high-value wins against strong opponents.
• Case networks built with k ≥ 4 show clear community structures, with communities exhibiting distinct win/loss patterns—some with high losing rates, others with high winning rates.
• Communities circled in red in the results indicate high losing rates, while green communities show high winning rates, validating the use of community-level win/loss as a difficulty proxy.
• The method successfully identifies thematic clusters of cases through shared legal article citations, enabling the grouping of legally similar judgments.
• The framework demonstrates that combining NLP, network analysis, and community detection can generate actionable, transparency-enhancing insights for non-legal users.