Similarity measure for Public Persons

TL;DR

This paper presents a method to measure the evolving relationships between public persons in news articles by analyzing time series of their mentions using correlation over sliding windows.

Contribution

It introduces a novel approach combining named entity extraction and time series correlation to assess dynamic relationships among public persons in news data.

Findings

Effective in capturing temporal relationship changes

Applicable to multilingual news datasets

Provides a quantitative measure of public persons' relationships

Abstract

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.