Natural Language Processing in Policy Evaluation: Extracting Policy Conditions from IMF Loan Agreements

Joakim Åkerström, Adel Daoud, Richard Johansson


Abstract
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.
Anthology ID:
W19-6134
Volume:
Proceedings of the 22nd Nordic Conference on Computational Linguistics
Month:
September–October
Year:
2019
Address:
Turku, Finland
Editors:
Mareike Hartmann, Barbara Plank
Venue:
NoDaLiDa
SIG:
Publisher:
Linköping University Electronic Press
Note:
Pages:
316–320
Language:
URL:
https://aclanthology.org/W19-6134
DOI:
Bibkey:
Cite (ACL):
Joakim Åkerström, Adel Daoud, and Richard Johansson. 2019. Natural Language Processing in Policy Evaluation: Extracting Policy Conditions from IMF Loan Agreements. In Proceedings of the 22nd Nordic Conference on Computational Linguistics, pages 316–320, Turku, Finland. Linköping University Electronic Press.
Cite (Informal):
Natural Language Processing in Policy Evaluation: Extracting Policy Conditions from IMF Loan Agreements (Åkerström et al., NoDaLiDa 2019)
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PDF:
https://aclanthology.org/W19-6134.pdf