Difference between revisions of "Learning to Identify Historical Figures for Timeline Creation from Wikipedia Articles"
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| − | '''Learning to Identify Historical Figures for Timeline Creation from Wikipedia Articles''' - scientific work related to Wikipedia quality published in 2014, written by Sandro Bauer, Stephen Clark and Thore Graepel. | + | '''Learning to Identify Historical Figures for Timeline Creation from Wikipedia Articles''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Sandro Bauer]], [[Stephen Clark]] and [[Thore Graepel]]. |
== Overview == | == Overview == | ||
| − | 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. Authors 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. Authors 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. | + | 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. Authors 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. Authors 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. |
Revision as of 11:19, 30 June 2019
Learning to Identify Historical Figures for Timeline Creation from Wikipedia Articles - scientific work related to Wikipedia quality published in 2014, written by Sandro Bauer, Stephen Clark and Thore Graepel.
Overview
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. Authors 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. Authors 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.
