Joakim Åkerström
Department of Computer Science and Engineering, University of Gothenburg, Sweden
Adel Daoud
Centre for Business Research, Cambridge Judge Business School, University of Cambridge, UK /
Harvard Center for Population and Development Studies, Harvard University, USA / The Alan Turing Institute, London, UK
Adel Daoud
Department of Computer Science and Engineering, University of Gothenburg, Sweden
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Published in: Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa), September 30 - October 2, Turku, Finland
Linköping Electronic Conference Proceedings 167:34, p. 316--320
NEALT Proceedings Series 42:34, p. 316--320
Published: 2019-10-02
ISBN: 978-91-7929-995-8
ISSN: 1650-3686 (print), 1650-3740 (online)
Social science researchers often use text as the raw data in investigations: for instance, when investigating the effects of IMF policies on the development of countries under IMF programs, researchers typically encode structured descriptions of the programs using a time-consuming manual effort. Making this process automatic may open up new opportunities in scaling up such investigations. As a first step towards automatizing this coding process, we describe an experiment where we apply a sentence classifier that automatically detects mentions of policy conditions in IMF loan agreements and divides them into different types. The results show that the classifier is generally able to detect the policy conditions, although some types are hard to distinguish.
