Automatic theory generation from analyst text files using coherence networks

Steven C. Shaffer

doi:10.1117/12.2049528

22 May 2014 Automatic theory generation from analyst text files using coherence networks

Steven C. Shaffer

Proceedings Volume 9122, Next-Generation Analyst II; 912202 (2014) https://doi.org/10.1117/12.2049528
Event: SPIE Sensing Technology + Applications, 2014, Baltimore, MD, United States

Abstract

This paper describes a three-phase process of extracting knowledge from analyst textual reports. Phase 1 involves performing natural language processing on the source text to extract subject-predicate-object triples. In phase 2, these triples are then fed into a coherence network analysis process, using a genetic algorithm optimization. Finally, the highest-value sub networks are processed into a semantic network graph for display. Initial work on a well- known data set (a Wikipedia article on Abraham Lincoln) has shown excellent results without any specific tuning. Next, we ran the process on the SYNthetic Counter-INsurgency (SYNCOIN) data set, developed at Penn State, yielding interesting and potentially useful results.

Citation Download Citation

Steven C. Shaffer "Automatic theory generation from analyst text files using coherence networks", Proc. SPIE 9122, Next-Generation Analyst II, 912202 (22 May 2014); https://doi.org/10.1117/12.2049528

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