Content Analysis for Proactive Intelligence: Marshaling Frame Evidence.

Conference · Sun Jul 01 00:00:00 EDT 2007

OSTI ID:931913

Sanfilippo, Antonio ^[1]; Cowell, Andrew ^[1]; Pouchard, Line Catherine ^[2]

---

1. Pacific Northwest National Laboratory (PNNL)
2. ORNL

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.

• Research Organization: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: Work for Others (WFO)
• DOE Contract Number: DE-AC05-00OR22725
• OSTI ID: 931913
• Resource Relation: Conference: 22nd Conference on Artificial Intelligence (AAAI 2007), Vancouver, Canada, 20070722, 20070726
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
ARTIFICIAL INTELLIGENCE
DATA ANALYSIS
INFORMATION NEEDS
MATHEMATICAL MODELS
INFORMATION RETRIEVAL
DATA ACQUISITION