Towards a Gold Standard Corpus for Variable Detection and Linking in Social Science Publications

Abstract: In this paper, we describe our effort to create a new corpus for the evaluation of detecting and linking so-called survey variables in social science publications (e.g., "Do you believe in Heaven?"). The task is to recognize survey variable mentions in a given text, disambiguate them, and link them to the corresponding variable within a knowledge base. Since there are generally hundreds of candidates to link to and due to the wide variety of forms they can take, this is a challenging task within NLP. The contribution of our work is the first gold standard corpus for the variable detection and linking task. We describe the annotation guidelines and the annotation process. The produced corpus is multilingual - German and English - and includes manually curated word and phrase alignments. Moreover, it includes text samples that could not be assigned to any variables, denoted as negative examples. Based on the new dataset, we conduct an evaluation of several state-of-the-art text class

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch
• Notes: Veröffentlichungsversion ; begutachtet (peer reviewed) ; In: Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC). 2018. ISBN 979-10-95546-00-9

Classification and Topics

• Classification: Sprache, Linguistik

Contributors, Places and Time

• Event: Veröffentlichung
• (where): Mannheim
• (when): 2018
• Creator: Zielinski, Andrea ; Mutschke, Peter
• Contributor: European Language Resources Association (ELRA)

Further information

• URN: urn:nbn:de:0168-ssoar-57723-2
• Rights: Open Access; Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:26 AM CEST