Content analysis for proactive intelligence: Marshaling frame evidence

Antonio Sanfilippo, A. J. Cowell, S. C. Tratz, A. M. Boek, A. K. Cowell, C. Posse, L. C. Pouchard

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the National Conference on Artificial Intelligence
Pages919-924
Number of pages6
Volume1
Publication statusPublished - 2007
Externally publishedYes
EventAAAI-07/IAAI-07 Proceedings: 22nd AAAI Conference on Artificial Intelligence and the 19th Innovative Applications of Artificial Intelligence Conference - Vancouver, BC
Duration: 22 Jul 200726 Jul 2007

Other

OtherAAAI-07/IAAI-07 Proceedings: 22nd AAAI Conference on Artificial Intelligence and the 19th Innovative Applications of Artificial Intelligence Conference
CityVancouver, BC
Period22/7/0726/7/07

ASJC Scopus subject areas

  • Software

Cite this

Sanfilippo, A., Cowell, A. J., Tratz, S. C., Boek, A. M., Cowell, A. K., Posse, C., & Pouchard, L. C. (2007). Content analysis for proactive intelligence: Marshaling frame evidence. In Proceedings of the National Conference on Artificial Intelligence (Vol. 1, pp. 919-924)