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%T Mining Social Science Publications for Survey Variables
%A Zielinski, Andrea
%A Mutschke, Peter
%P 47-52
%D 2017
%K OpenMinTed
%> https://nbn-resolving.org/urn:nbn:de:0168-ssoar-57722-7
%U http://www.aclweb.org/anthology/W17-2907
%X 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.
%G en
%9 Konferenzbeitrag
%W GESIS - http://www.gesis.org
%~ SSOAR - http://www.ssoar.info