Military Communications and Information Technology: A Trusted ...

278 Military Communications and Information Technology...
c) Automatic processing and exploitation, and analysis: Translation output of higher
quality can serve as input for automatic document processing and content
analysis. If it is important to efficiently or to fully automatically process documents,
e.g. where a vast amount of data needs to be analyzed, automatic translation
might be the only way to exploit the whole data. In this case, the documents
collected can be translated by an SMT system and the translations serve
as the input for further NLP processing such as information extraction, text
mining or opinion mining. The quality of the final NLP output then highly
depends on the translation quality.
Our approach claims that machine translation is highly useful for the optimal
analysis of large amounts of foreign material. It can be applied to assist the human
analyst and can generate translations that can serve as input for further automatic
NLP processing. As regards the latter, the quality of the generated translations
will determine what kind of further automatic analysis can be deployed. Therefore,
the extent to that text in different languages can be processed automatically by
NLP applications depends on the quality of the generated translations as well as on
the nature and difficulty of the further NLP tasks. And, if a vast amount of textual
data in different languages needs to be processed, i.e., on the internet or from
other document collections, the application of MT tools might be the only way to
process the data.
V. Conclustions
This paper discussed that NLP technology can strongly support military intelligence
with the processing of information. Textual data that needs to be analyzed
for intelligence purpose includes not only documents collected by human intelligence
but also open source documents. Especially, the internet today carries a lot
of information that may be relevant in the context of military operations. Overall,
the amount of documents that needs to be processed for intelligence purposes
is extremely large. The manual analysis of textual data is very labor-intensive and
time-consuming and in some cases not even practicable. This problem is even worse
if documents in different languages have to be processed as there may be only few or
no translators available. This work argued that NLP intelligence tools can be applied
for the efficient and effective content analysis of documents in different languages.
We described how NLP technology can be applied to support the intelligence
process on tasks that involve the processing of text. For the collection of relevant
documents from intelligence document collections or open source databases, such
as the internet, NLP tools for automatic information retrieval and document classification
can be utilized. Texts can be processed and information can be identified,
extracted and analyzed by NLP technologies such as information extraction, text
mining and opinion mining. For the processing of documents in foreign languages