Similarity measure for Public Persons
This work addresses the need for dynamic relationship analysis in news media, but it is incremental as it applies existing techniques to a specific domain.
The authors tackled the problem of measuring relationships between public persons over time by developing a method that uses named entity extraction and Pearson correlation on time series of person occurrences in news articles, resulting in a time-dependent similarity measure for a webportal.
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.