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

Conference paper
Authors Joakim Åkerström
Adel Daoud
Richard Johansson
Published in Proceedings of the 22nd Nordic Conference on Computational Linguistics; September 30 – October 2; Turku, Finland
Publisher Linköping University Electronic Press
Publication year 2019
Published at Department of Computer Science and Engineering (GU)
Language en
Links https://www.aclweb.org/anthology/W1...
Keywords natural language processing, text categorization, IMF, International Monetary Fund, methodology
Subject categories Computational linguistics, Other Social Sciences

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.

Page Manager: Webmaster|Last update: 9/11/2012
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