Content analysis for proactive intelligence: marshaling frame evidence

Authors:
A. P. Sanfilippo;A. J. Cowell;S. C. Tratz;A. M. Boek;A. K. Cowell;C. Posse;L. C. Pouchard
Affiliations:
Pacific Northwest National Laboratory, Richland, WA;Pacific Northwest National Laboratory, Richland, WA;Pacific Northwest National Laboratory, Richland, WA;Pacific Northwest National Laboratory, Richland, WA;Pacific Northwest National Laboratory, Richland, WA;Pacific Northwest National Laboratory, Richland, WA;Oak Ridge National Laboratory, Oak Ridge, TN
Venue:
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Year:
2007

Citing 3
Cited 1

A semantic approach to IE pattern induction

ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Word domain disambiguation via word sense disambiguation

NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
A high accuracy method for semi-supervised information extraction

NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers

Technosocial predictive analytics for illicit nuclear trafficking

SBP'11 Proceedings of the 4th international conference on Social computing, behavioral-cultural modeling and prediction

Quantified Score

Hi-index	0.00

Visualization

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.