  For the webportal "Who is in the News!" with statistics about the appearence
of persons in written news we developed an extension, which measures the
relationship of public persons depending on a time parameter, as the
relationship may vary over time. On a training corpus of English and German
news articles we built a measure by extracting the persons occurrence in the
text via pretrained named entity extraction and then construct time series of
counts for each person. Pearson correlation over a sliding window is then used
to measure the relation of two persons.
