From Narratives to Conceptual Models via Natural Language Processing

This paper explores the use of natural language processing (NLP) towards the semi-automatic generation of conceptual models, and eventual simulation specifications, from descriptions of a phenomenon. Narratives describing the problem are transformed into a list of concepts and relationships and visualized using a network graph. The process relies on pattern-based grammatical rules and an NLP dependency parser identifying important concept types, namely actors, factors, and mechanisms. We use three conceptualizations, created by potential users, to understand how the NLP-generated model should and could be adjusted. The objective of the research is to develop potential standard approaches users can use to generate conceptual models; develop a conceptual modeling assistant that subject matter experts can use to make them participant in the simulation creation process; and to identify how narratives should be written so an NLP-based conceptual modeling assistant may provide a thorough description of a phenomenon.