Learning to Identify Historical Figures for Timeline Creation from Wikipedia Articles

This paper addresses a central sub-task of timeline creation from historical Wikipedia articles: learning from text which of the person names in a textual article should appear in a timeline on the same topic. We first process hundreds of timelines written by human experts and related Wikipedia articles to construct a corpus that can be used to evaluate systems that create history timelines from text documents. We then use a set of features to train a classifier that predicts the most important person names, resulting in a clear improvement over a competitive baseline.