Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Extraction of Temporal Networks from Term Co-occurrences in Online
  Textual Sources   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.

17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat
18
The categories of this paper are: econ
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph