Mining Social Science Publications for Survey Variables
| Autor: | Zielinski, Andrea, Mutschke, Peter |
|---|---|
| Přispěvatelé: | Association for Computational Linguistics (ACL) |
| Rok vydání: | 2017 |
| Předmět: |
Literatur
Rhetorik Literaturwissenschaft Publizistische Medien Journalismus Verlagswesen Literature rhetoric and criticism News media journalism publishing OpenMinTed Information Science Science of Literature Linguistics Literaturwissenschaft Sprachwissenschaft Linguistik Informationswissenschaft publication technical literature artificial intelligence computational linguistics survey social science concept algorithm periodical construction of indicators data capture Datengewinnung künstliche Intelligenz Begriff Algorithmus Computerlinguistik Befragung Publikation Sozialwissenschaft Fachliteratur Indikatorenbildung Zeitschrift 30200 50200 |
| Zdroj: | Proceedings of the Second Workshop on NLP and Computational Social Science, 47-52 |
| Druh dokumentu: | Konferenzbeitrag<br />conference paper<br />conferenceObject |
| Popis: | 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. |
| Databáze: | SSOAR – Social Science Open Access Repository |
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