Automatic theory generation from analyst text files using coherence networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationNext-Generation Analyst II
PublisherSPIE
ISBN (Print)9781628410594
DOIs
StatePublished - Jan 1 2014
EventNext-Generation Analyst II - Baltimore, MD, United States
Duration: May 6 2014May 6 2014

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume9122
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Other

OtherNext-Generation Analyst II
CountryUnited States
CityBaltimore, MD
Period5/6/145/6/14

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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  • Cite this

    Shaffer, S. C. (2014). Automatic theory generation from analyst text files using coherence networks. In Next-Generation Analyst II [912202] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 9122). SPIE. https://doi.org/10.1117/12.2049528