The immense amount of information that can be collected from open sources have become increasingly important to the academic and business communities over the past two decades Also national and global security is becoming more and more reliant on rapidly making sense of and managing the relevant intelligence data The enormous size and complexity of the gathered data require advanced and unique data storage, management, analysis, and visualization technologies The technologies and applications currently adopted typically involve elements like text mining, natural language processing, and stochastic-based algorithms Natural language processing, in particular, has been a prominent research topic for many years now We have conceptualized an analysis framework with a strong focus on various techniques of natural language processing to aggregate, manipulate, and analyze intelligence information

http://ieeexplore.ieee.org/iel7/6685213/6695491/06695527.pdf

NLP as an essential ingredient of effective OSINT frameworks

Intelligence plays an important role in supporting military operations. In the course of military intelligence a vast amount of textual data in different languages needs to be analyzed. In addition to information provided by traditional military intelligence, nowadays the internet offers important resources of potential militarily relevant information. However, we are not able to manually handle this vast amount of data. The science of natural language processing (NLP) provides technology to efficiently handle this task, in particular by means of machine translation and text mining. In our research project ISAF-MT we created a statistical machine translation (SMT) system for Dari to German. In this paper we describe how NLP technologies and in particular SMT can be applied to different intelligence processes. We therefore argue that multilingual NLP technology can strongly support military operations.

http://ieeexplore.ieee.org/iel5/6375511/6387877/06387890.pdf

Automatic exploitation of multilingual information for military intelligence purposes

: Written information for military purposes is available in abundance. Documents are written in many languages. The question is how we can automate the content extraction of these documents. One possible approach is based on shallow parsing (information extraction) with application specific combination of analysis results. One example of this, the ZENON research system, does a partial content analysis of some English, Dari, and Tajik texts. Another principal approach for content extraction is based on a combination of deep and shallow parsing with logical inferences on the analysis results. In the project "Multilingual content analysis with semantic inference on military relevant texts" (mIE) we followed the second approach. In this paper, we present the results of the mIE project. First, we briefly contrast the ZENON project to the mIE project. In the main part of the paper, the mIE project is presented. After explaining the combined deep and shallow parsing approach with Head-driven Phrase Structured Grammars, the inference process is introduced. Then we show how background knowledge (WordNet, YAGO) is integrated into the logical inferences to increase the extent, quality, and accuracy of the content extraction. The prototype also is presented. The presentation includes briefing charts.

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Multilingual Content Extraction Extended with Background Knowledge for Military Intelligence

: The current deployments of the German Federal Armed Forces make it necessary to analyze large quantities of intelligence reports and other documents written in different languages. Natural language processing techniques (NLP) can be applied to efficiently handle these tasks. The ZENON project makes use of an information extraction approach for the (partial) content analysis of English HUMINT reports. It has been extended to do multilingual information extraction (i.e., processing Dari and Tajik texts). This paper focuses on the improvement of ZENON's English semantic analysis. To extend the system's coverage when performing content analysis the authors used a semantic role labeling approach. The paper describes the ZENON system and its information extraction functions, the semantic role labeling approach, and the architecture of the implemented application. The presentation includes briefing charts.

Matthias Hecking

Papers

Multilingual Content Extraction Extended with Background Knowledge for Military Intelligence

Semantic Analysis of Military Relevant Texts for Intelligence Purposes

Automatic exploitation of multilingual information for military intelligence purposes