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Using NMF for analyzing war logs

: Thorleuchter, Dirk; Poel, Dirk van den

Preprint urn:nbn:de:0011-n-2195355 (190 KByte PDF)
MD5 Fingerprint: f30f8afe5d140f7b11854674cde0c0e7
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Created on: 6.12.2012

Aschenbruck, N. (Ed.); Martini, P.; Meier, M.; Tölle, J.:
Future Security. 7th Security Research Conference 2012. Proceedings : Bonn, Germany, September 4-6, 2012
Berlin: Springer, 2012 (Communications in computer and information science 318)
ISBN: 978-3-642-33160-2 (Print)
ISBN: 978-3-642-33161-9 (Online)
ISBN: 3-642-33160-2
ISSN: 1865-0929
Security Research Conference "Future Security" <7, 2012, Bonn>
Conference Paper, Electronic Publication
Fraunhofer INT ()
non-negative matrix factorization; NMF; text mining

We investigate a semi-automated identification of technical problems occurred by armed forces weapon systems during mission of war. The proposed methodology is based on a semantic analysis of textual information in reports from soldiers (war logs). Latent semantic indexing (LSI) with non-negative matrix factorization (NMF) as technique from multivariate analysis and linear algebra is used to extract hidden semantic textual patterns from the reports. NMF factorizes the term-by-war log matrix - that consists of weighted term frequencies – into two non-negative matrices. This enables natural parts-based representation of the report information and it leads to an easy evaluation by human experts because human brain also uses parts-based representation. For an improved research and technology planning, the identified technical problems are a valuable source of information. A case study extracts technical problems from military logs of the Afghanistan war. Results are compared to a manual analysis written by journalists of "Der Spiegel".