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            <title>Proceedings of the Second Workshop on NLP and Computational Social Science</title>
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            <namePart type="given">Dirk</namePart>
            <namePart type="family">Hovy</namePart>
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            <namePart type="given">Svitlana</namePart>
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            <namePart type="given">Oren</namePart>
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    <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 a significant increase in performance over the random baseline.</abstract>
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%0 Conference Proceedings
%T Mining Social Science Publications for Survey Variables
%A Zielinski, Andrea
%A Mutschke, Peter
%Y Hovy, Dirk
%Y Volkova, Svitlana
%Y Bamman, David
%Y Jurgens, David
%Y O’Connor, Brendan
%Y Tsur, Oren
%Y Doğruöz, A. Seza
%S Proceedings of the Second Workshop on NLP and Computational Social Science
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F zielinski-mutschke-2017-mining
%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 a significant increase in performance over the random baseline.
%R 10.18653/v1/W17-2907
%U https://aclanthology.org/W17-2907
%U https://doi.org/10.18653/v1/W17-2907
%P 47-52

Markdown (Informal)

[Mining Social Science Publications for Survey Variables](https://aclanthology.org/W17-2907) (Zielinski & Mutschke, NLP+CSS 2017)

• Mining Social Science Publications for Survey Variables (Zielinski & Mutschke, NLP+CSS 2017)

ACL

• Andrea Zielinski and Peter Mutschke. 2017. Mining Social Science Publications for Survey Variables. In Proceedings of the Second Workshop on NLP and Computational Social Science, pages 47–52, Vancouver, Canada. Association for Computational Linguistics.