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