model[NL]generation
Lars Ackermann, Bernhard Volz · 2013
In this paper, we describe a novel approach of extracting models from natural language text sources. This requires linguistic analysis as well as techniques for interpreting and using the analysis results. Our linguistic analysis engine provides feature analysis for a rule-based model element detection. Furthermore, the presented approach enables users to generate domain- and application-specific model element detection rules based on natural language sample sentences. Detection rules also have to be connected to instantiation rules for the respective type of model element. This is done through a highly system-supported mapping step where users are able to choose elements from arbitrary meta models and to connect their properties with functions over natural language sentence parts. As both, the definition and application of detection rules is always a sensitive balancing act between precision and recall, these steps are highly interactive. That is why our current prototype also supports detection rule adaption and iterative rule set completion -- always to the level of current need.