Bibtex export


@incollection{ Zielinski2017,
 title = {Mining Social Science Publications for Survey Variables},
 author = {Zielinski, Andrea and Mutschke, Peter},
 year = {2017},
 booktitle = {Proceedings of the Second Workshop on NLP and Computational Social Science},
 pages = {47-52},
 publisher = {Association for Computational Linguistics (ACL)},
 urn = {},
 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.},
 keywords = {publication; technical literature; Datengewinnung; künstliche Intelligenz; artificial intelligence; computational linguistics; survey; social science; Begriff; concept; Algorithmus; Computerlinguistik; Befragung; Publikation; Sozialwissenschaft; Fachliteratur; algorithm; periodical; Indikatorenbildung; construction of indicators; data capture; Zeitschrift}}