Mining Social Science Publications for Survey Variables
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...
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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.
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Towards a Gold Standard Corpus for Variable Detection and Linking in Social Science Publications
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...
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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 classification and textual similarity methods. The annotated corpus is made available along with an open-source baseline system for variable mention identification and linking.
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