@inproceedings{64559d1eba794c0db2afe87f40c30351,
title = "Content analysis for proactive intelligence: Marshaling frame evidence",
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 on the acquisition and marshaling of frames from unstructured text.",
author = "Sanfilippo, {A. P.} and Cowell, {A. J.} and Tratz, {S. C.} and Boek, {A. M.} and Cowell, {A. K.} and C. Posse and Pouchard, {L. C.}",
year = "2007",
language = "English",
isbn = "1577353234",
series = "Proceedings of the National Conference on Artificial Intelligence",
pages = "919--924",
booktitle = "AAAI-07/IAAI-07 Proceedings",
note = "AAAI-07/IAAI-07 Proceedings: 22nd AAAI Conference on Artificial Intelligence and the 19th Innovative Applications of Artificial Intelligence Conference ; Conference date: 22-07-2007 Through 26-07-2007",
}