PNNL

Abstract

Modeling and simulation have great potential as technologies capable of aiding analysts in making accurate predictions of future situations to help provide competitive advantage and avoid strategic surprise. However, to make modeling and simulation effective, an evidence marshaling process is needed that addresses the information needs of the modeling task, as detailed by subject matter experts. We suggest that such an evidence marshaling process can be obtained by combining natural language processing and content analysis techniques to provide quantified qualitative content assessments, and describe a case study with specific reference to the acquisition and marshaling of frames from unstructured text.