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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
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Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 1
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portfolio
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publications
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
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
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
talks
Talk 1 on Relevant Topic in Your Field
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This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Descriptive Methods of Data Mining
Undergraduate course, Nova IMS, 2021
This course addressed the topics and issues typically associated with “data mining” or “knowledge discovery”. Its primary goal is to allow students to gather skills for extracting information and knowledge from large databases. The skills developed include databases, data processing, prescriptive analysis, and data visualization.
Data Science for Marketing
Postgraduate course, Nova IMS, 2021
This course familiarizes students with Data science concepts, applications, and projects’ lifecycles. It involves statistics, data visualization, database systems, and machine learning. Students learn data preparation before building analytical models, such as data description, RFM, or association rules (e.g., market basket analysis).
Business Cases with Data Science
Masters course, Nova IMS, 2022
In this course, students were guided through six projects involving different topics in data science, such as language processing, recommendation systems, predictive analysis, and data visualization.