  A stream of unstructured news can be a valuable source of hidden relations
between different entities, such as financial institutions, countries, or
persons. We present an approach to continuously collect online news, recognize
relevant entities in them, and extract time-varying networks. The nodes of the
network are the entities, and the links are their co-occurrences. We present a
method to estimate the significance of co-occurrences, and a benchmark model
against which their robustness is evaluated. The approach is applied to a large
set of financial news, collected over a period of two years. The entities we
consider are 50 countries which issue sovereign bonds, and which are insured by
Credit Default Swaps (CDS) in turn. We compare the country co-occurrence
networks to the CDS networks constructed from the correlations between the CDS.
The results show relatively small, but significant overlap between the networks
extracted from the news and those from the CDS correlations.
