Automatic text structuring and retrieval-experiments in automatic encyclopedia searching
SIGIR '91 Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval
A vector space model for automatic indexing
Communications of the ACM
Modern Information Retrieval
Human terrain data: what should we do with it?
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Agent-based modeling with social networks for terrorist recruitment
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Developing social networks for artificial societies from survey data
SBP'10 Proceedings of the Third international conference on Social Computing, Behavioral Modeling, and Prediction
Developing cognitive models for social simulation from survey data
SBP'10 Proceedings of the Third international conference on Social Computing, Behavioral Modeling, and Prediction
| Hi-index | 0.00 |
Cognitive social simulation provides a promising means of gaining insight into a particular area of interest for a given population, but the data to instantiate these models must be gleaned from disparate data sources. Methods and tools to efficiently leverage information regarding a population of interest from a document corpus are required. This paper provides an overview of the use of Sandia National Laboratory's Text Analysis Extensible Library (STANLEY) to categorize a body of documents to create Bayesian belief networks that can be used as cognitive models within social simulation.