From Court Verdicts to Structured Data: An LLM-Assisted Pipeline for Annotation [ LingMon #233 ]

📅 09/03/2026 | 🗣 Jakub Drápal (PF UK) and Ivana Kvapilíková (ÚFAL MFF UK) | 📝 From Court Verdicts to Structured Data: An LLM-Assisted Pipeline for Annotation and Clustering 🔖 ABOUT THE LECTURE ▔▔▔▔▔▔▔▔▔▔▔▔▔ ▸Court verdicts contain rich information about criminal behavior, but their unstructured format limits systematic analysis. We present an LLM-assisted pipeline for constructing structured datasets from factual statements contained in the verdict texts, combining attribute exploration, span annotation, and value clustering. Attributes are partly defined by legal experts and partly induced from the data, enabling a comparison between expert-defined and data-driven schema design. We validate our results on the Slovak dataset through careful manual evaluation, highlighting both the strengths and current limitations of the proposed pipeline and their impact on downstream legal analytics. 📽️ ABOUT THE SERIES ▔▔▔▔▔▔▔▔▔▔▔▔ ▸The history of Linguistic Mondays dates back to the 1980s and their original aim was to make both the students and the faculty members as well as the wider research community aware of the field of computational linguistics in general and of the results achieved by the members of our team in particular. During the years, with the growing awareness of the domain and with new trends appearing on the scene and with more master and doctoral students coming in, the scope of the topics introduced has broadened correspondingly, covering all aspects of the field from the basics of computational linguistics, its linguistic and formal background through corpora case studies and natural language processing applications such as machine translation and information retrieval up to the most modern trends including machine learning. It is also offers an excellent opportunity for PhD students to present their results and to receive a relevant response from leading experts in the field. ❔ SOCIAL MEDIA ▔▔▔▔▔▔▔▔▔▔ ▸web: https://ufal.mff.cuni.cz ▸facebook: https://www.facebook.com/UFALMFFUK ▸twitter: https://twitter.com/ufal_cuni