Military Communications and Information Technology: A Trusted ...

268 Military Communications and Information Technology...
is being carried out through the social media [11]. The large involvement of terrorist
activities in the internet indicates the urgency to monitor internet data for
security and defense issues.
As the use of web technologies is growing its information content is growing,
too. Therefore, the probability that information relevant for military purpose
is present in the internet is increasing, too. Hence, it is essential to be able to extract
information not only from traditional intelligence sources but also from the internet.
Due to the vast quantity of possibly important documents, intelligence tools
are needed [1]. These tools must help analysts to efficiently and rapidly receive
relevant documents from the internet and collected data sources, translate them
if necessary, extract critical information and analyze them.
III. Natural language processing for intelligence purposes
Natural language processing (NLP) is an active research field that combines
computer science with linguistics. Through the application of different techniques
from computer science, natural language text or speech is processed. As this paper
focuses on the processing of text, in the rest of this paper only “text” will be used,
although in all cases, NLP research exists that also looks into the corresponding
processing of speech.
In general, NLP approaches are either based on rules or on machine learning.
Rule-based approaches apply a set of hand-written rules that indicate how to
process the input text. Machine learning is a technique from the field of artificial
intelligence (AI). NLP approaches that are based on machine learning usually apply
statistical models to analyze the input. Such models are automatically learned (or
trained) based on example data (training data). After training, the system is able to
analyze new input. This means, the system derives the most probable analysis based
upon the trained statistical model. For example, in the course of statistical machine
translation (SMT), translations are generated according to the translation model. This
model is trained on a parallel corpus, a collection of texts that represent translations
of each other in both languages of interest. During training the system learns how
to translate source language text into target language text based on the probability
distribution of the training data. The trained SMT system is then able to find the most
probable translation in the target language given the source language input text.
Both rule-based and statistical NLP techniques have different advantages and
disadvantages. Rule-based NLP technology is usually more accurate than statistical
approaches, as it processes everything based on rules. Statistical systems always
output the most probable result which is not necessarily the correct result. Statistical
approaches, however, also have different advantages especially with respect to military
applications: in general, statistical systems have a higher coverage, they tend to
be more robust, and they can be produced rapidly and more cost efficiently. Language
is highly irregular and dynamic. This leads to the fact that it is almost impossible to