@inproceedings{fb266a323bee48ebb65a9b7c6d642c2c,
title = "Automatic theory generation from analyst text files using coherence networks",
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.",
author = "Shaffer, {Steven C.}",
year = "2014",
doi = "10.1117/12.2049528",
language = "English (US)",
isbn = "9781628410594",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
booktitle = "Next-Generation Analyst II",
address = "United States",
note = "Next-Generation Analyst II ; Conference date: 06-05-2014 Through 06-05-2014",
}