Automated Knowledge Extraction from Legal Texts using ASKE

n this paper, we present the ASKE (Automated System for Knowledge Extraction) approach to legal knowledge extraction, based on a combination of context-aware embedding models and zero-shot learning techniques into a three-phase extraction cycle, which is executed a number of times to progressively extract concepts representative of the different meanings of terminology used in legal documents chunks. We show ASKE in action in a case study of legal knowledge extraction from a real corpus of case law decisions in the framework of the NGUPP project.