Under CopyrightThorleuchter, DirkDirkThorleuchterPoel, Dirk van denDirk van denPoel2022-03-126.12.20122012https://publica.fraunhofer.de/handle/publica/37687510.1007/978-3-642-33161-9_1210.24406/publica-r-3768752-s2.0-84867688363We 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".ennon-negative matrix factorizationNMFtext mining620conference paper