Hugo Silva

Hugo Silva

Ph.D. Candidate @ Nova IMS, Data Scientist @ SUSEP, Co-Founder @ Arteh

Publications

Automation of Legal Precedents Retrieval: Findings from a Literature Review

Published in International Journal of Intelligent Systems, 2023

This review article dives deep into the evolving landscape of automating the identification of legal precedents. It describes details of two “eras” of precedent retrieval automation, spotlighting the revolution powered by natural language processing (NLP) and machine learning (ML) until the use of Transformers. We also described the usual ML pipeline for precedent retrieval, the multiple techniques used, the data and geographies involved, and proposed a taxonomy for this field. A gap in validation and real-world deployments became evident, and a key question echoes: will courts of justice transform precedent searches through automation?

Recommended citation: Hugo Mentzingen, Nuno António, Fernando Bacao, "Automation of Legal Precedents Retrieval: Findings from a Literature Review", International Journal of Intelligent Systems, vol. 2023, Article ID 6660983, 22 pages, 2023. https://doi.org/10.1155/2023/6660983

Joining metadata and textual features to advise administrative courts decisions: a cascading classifier approach

Published in Artificial Intelligence and Law, 2023

Assessing the decisions made by regulatory bodies and courts is pivotal, given the vast influence they hold on the people. While machine learning (ML) may be used to predict such decisions, prevalent studies often overlook factors like consistency, real-world applicability, generality, and explainability. Our research introduces a unique two-stage cascade classifier model that harnesses both textual features and metadata from proceedings to improve performance. Utilizing the SHapley Additive exPlanations (SHAP) mechanism, our model remains transparent and explainable. With our approach, we’ve achieved a weighted F1 score of 0.900, outstripping baselines.

Recommended citation: Mentzingen, H., Antonio, N. & Lobo, V. Joining metadata and textual features to advise administrative courts decisions: a cascading classifier approach. Artif Intell Law (2023). https://link.springer.com/article/10.1007/s10506-023-09348-9

A Rapid Semi-automated Literature Review on Legal Precedents Retrieval

Published in EPIA Conference on Artificial Intelligence, 2022

This work uses text mining (TM), natural language processing (NLP), and data visualization methods to provide a semi-automated rapid literature review and identify how justice courts and legal practitioners may use AI to retrieve similar cases. Based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), automation techniques were used to expedite the literature review. In this study, we confirmed the feasibility of automation tools for expediting literature reviews and provided an overview of the current research state on legal precedents retrieval.

Recommended citation: Silva, H., António, N., Bacao, F. (2022). A Rapid Semi-automated Literature Review on Legal Precedents Retrieval. In: Marreiros, G., Martins, B., Paiva, A., Ribeiro, B., Sardinha, A. (eds) Progress in Artificial Intelligence. EPIA 2022. Lecture Notes in Computer Science(), vol 13566. Springer, Cham. https://doi.org/10.1007/978-3-031-16474-3_5