Automatic theory generation from analyst text files using coherence networks
|Automatic theory generation from analyst text files using coherence networks|
|Published in||Proceedings of SPIE - The International Society for Optical Engineering|
|Keyword(s)||Coherence, Natural language, SYNCOIN, Text analysis (Extra: Coherent light, Genetic algorithms, Coherence networks, Genetic-algorithm optimizations, NAtural language processing, Natural languages, Semantic network, SYNCOIN, Text analysis, Wikipedia articles, Natural language processing systems)|
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Automatic theory generation from analyst text files using coherence networks is a 2014 conference paper written in English by Shaffer S.C. and published in Proceedings of SPIE - The International Society for Optical Engineering.
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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