Mining Social Science Publications for Survey Variables

Conference paper | Konferenzbeitrag

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.

Object Details

• Extent: Seite(n): 47-52
• Language: Englisch
• Notes: Status: Postprint; begutachtet (peer reviewed)

References and Relationships

• Bibliographic citation: Proceedings of the Second Workshop on NLP and Computational Social Science

Classification and Topics

• Subject: Literatur, Rhetorik, Literaturwissenschaft ; Publizistische Medien, Journalismus,Verlagswesen ; Literaturwissenschaft, Sprachwissenschaft, Linguistik ; Informationswissenschaft ; Datengewinnung ; künstliche Intelligenz ; Begriff ; Algorithmus ; Computerlinguistik ; Befragung ; Publikation ; Sozialwissenschaft ; Fachliteratur ; Indikatorenbildung ; Zeitschrift

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Zielinski, Andrea ; Mutschke, Peter
• Event: Veröffentlichung
• (who): Association for Computational Linguistics (ACL)
• (when): 2017

Further information

• URN: urn:nbn:de:0168-ssoar-57722-7
• Rights: GESIS - Leibniz-Institut für Sozialwissenschaften. Bibliothek Köln
• Last update: 21.06.2024, 4:27 PM CEST