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.
CITATION STYLE
Bauer, S., Clark, S., & Graepel, T. (2015). Learning to identify historical figures for timeline creation from wikipedia articles. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8852, pp. 234–243). Springer Verlag. https://doi.org/10.1007/978-3-319-15168-7_30
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