The role of legal technology in the harmonisation of EU law

This dissertation explores the transformative impact of legal technology, particularly natural language processing and knowledge modelling, on the analysis of legal harmonisation within the European Union. From a legal-linguistic perspective, this first involves analysing the key constituents of the phenomenon, including multilingualism, the nature of language and legislative sources, and legal interpretation, all of which play a pivotal role in shaping the harmonisation landscape. The core of the thesis, however, encompasses various hybrid legal technology methodologies for effectively analysing legal harmonisation within the European Union. The dissertation illustrates impactful applications through case studies, particularly in the context of EU-funded projects that enhance judicial cooperation across European Member States. By bridging legal and technological domains, the thesis contributes to understanding how digital tools, when implemented within robust methodological frameworks, can concretely enhance EU legal harmonisation, especially through the most recent generative AI technologies. Moving from traditional vector-based computational pipelines to network analysis, ontological modelling, and language models-based approaches, this research proposes methodologies that, while accounting for the complexity of the phenomenon, can have a meaningful, concrete impact on the workload and capacity of European public administrations, with additional positive effects on legal practice. The outcome of this research, conducted in the rigorous legal domain where results must meet the highest standards, is further validated by legal experts through manual knowledge modelling and their validation and discussion of the results. The conclusions assess the challenges and prospects of deploying Large Language Models (LLMs) and related technologies in analysing legal harmonisation, while also evaluating research findings. These cutting-edge technologies hold promise for accelerating the comparison and exploration of multilingual legislative texts. Despite these advancements, incorporating accurate, manually-crafted data remains essential. This approach is crucial for minimizing the risk of producing misleading and inaccurate information.