Difference between revisions of "Learning to Identify Historical Figures for Timeline Creation from Wikipedia Articles"
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| + | | title = Learning to Identify Historical Figures for Timeline Creation from Wikipedia Articles | ||
| + | | date = 2014 | ||
| + | | authors = [[Sandro Bauer]]<br />[[Stephen Clark]]<br />[[Thore Graepel]] | ||
| + | | doi = 10.1007/978-3-319-15168-7_30 | ||
| + | | link = https://link.springer.com/content/pdf/10.1007%2F978-3-319-15168-7_30.pdf | ||
| + | }} | ||
'''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 08:35, 9 November 2019
Authors | Sandro Bauer Stephen Clark Thore Graepel |
|---|---|
Publication date | 2014 |
DOI | 10.1007/978-3-319-15168-7_30 |
Links | Original |
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.
