Contextual Analysis of Case Law: Enhancing Precedent Identification with Artificial Intelligence
Year: 2025
Type: dissertation
Abstract: In the field of Legal Informatics and Artificial Intelligence, this research addresses the challenge of efficiently identifying relevant legal precedents and understanding their context, an issue stemming from the current lack of effective end-to-end automated retrieval methods combined with visual analytics and contemporary AI assistance. To fill this gap, the study explores, evaluates, and integrates state-of-the-art language models and text embeddings to enhance precedent identification and contextual analysis of case law. The research encompassed a systematic literature review to establish the foundations and survey empirical methods for automated precedent retrieval. Further experiments employed text summarization and large language models to increase precedent identification performance and reduce processing time and costs. In parallel, a review of visual analytics of legal corpora informed the design of interactive visualizations to contextualize decisions from an administrative court. All these insights were integrated into a prototype application called JurisMap, which combines advanced NLP with intuitive visual interfaces, a chatbot and AI agents to assist legal professionals in precedent research and administrative defense preparation. The results demonstrate that classical and neural models each offer complementary strengths across different performance metrics, that summarization significantly improves retrieval efficiency, and that visualization enhances the interpretability of case law context. Ultimately, this research contributes a comprehensive, experimentally validated, and practically deployable framework for AI-assisted precedent retrieval and contextual analysis, effectively bridging theoretical developments in Legal Informatics with real-world legal practice.
Author: Silva, Hugo Saisse Mentzingen da
Language: English
Subfield: Law
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
Funder: European Commission
OpenAlex ID: W7111558496