Mining Social Science Publications for Survey Variables
Abstract: Research in Social Science is usually based on survey data where individual research questions relate to observable concepts (variables). However, due to a lack of standards for data citations a reliable identification of the variables used is often difficult. In this paper, we present a work-in-progress study that seeks to provide a solution to the variable detection task based on supervised machine learning algorithms, using a linguistic analysis pipeline to extract a rich feature set, including terminological concepts and similarity metric scores. Further, we present preliminary results on a small dataset that has been specifically designed for this task, yielding modest improvements over the baseline
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource, 47-52 S.
- Language
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Englisch
- Notes
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Postprint
begutachtet (peer reviewed)
In: Proceedings of the Second Workshop on NLP and Computational Social Science. 2017. S. 47-52
- Event
-
Veröffentlichung
- (where)
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Mannheim
- (when)
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2017
- Creator
- Contributor
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Association for Computational Linguistics (ACL)
- URN
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urn:nbn:de:0168-ssoar-57722-7
- Rights
-
Open Access; Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
25.03.2025, 1:51 PM CET
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Zielinski, Andrea
- Mutschke, Peter
- Association for Computational Linguistics (ACL)
Time of origin
- 2017
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