Towards the Automatic Extraction of Decision Model & Notation from Dutch Legal Text - van de Hoef, Annemae; Leewis, Sam; Smit, Koen (38th Bled eConference: Empowering Transformation: Shaping Digital Futures for All: Conference Proceedings, 2025)
The translation from laws and regulations into actionable business rules remains challenging due to the complexity of Dutch legal text. In addition, the (semi-)manual translation of law into business rules is both time-consuming and error-prone. To address these issues, this research explores the use of Natural Language Processing (NLP) to automatically extract legal decisions and represent them in a Decision Model and Notation (DMN) model. For this purpose, existing research was reviewed to define requirements, which formed the basis for the NLP prototype. The current prototype evaluates an existing approach and aims to process unstructured Dutch legal text. However, a theoretical extension is proposed to address the structural complexity of extracting a DMN model from structured Dutch legal texts. Therefore, future research should focus on implementing the proposed approach and validating it in collaboration with legal analysts to extract a DMN model from structured Dutch legal texts.