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    <titleInfo>
        <title>Natural Language Processing in Policy Evaluation: Extracting Policy Conditions from IMF Loan Agreements</title>
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    <name type="personal">
        <namePart type="given">Joakim</namePart>
        <namePart type="family">Åkerström</namePart>
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    <name type="personal">
        <namePart type="given">Adel</namePart>
        <namePart type="family">Daoud</namePart>
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    <name type="personal">
        <namePart type="given">Richard</namePart>
        <namePart type="family">Johansson</namePart>
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        <dateIssued>2019-sep–oct</dateIssued>
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        <titleInfo>
            <title>Proceedings of the 22nd Nordic Conference on Computational Linguistics</title>
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        <name type="personal">
            <namePart type="given">Mareike</namePart>
            <namePart type="family">Hartmann</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Barbara</namePart>
            <namePart type="family">Plank</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <originInfo>
            <publisher>Linköping University Electronic Press</publisher>
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                <placeTerm type="text">Turku, Finland</placeTerm>
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    <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.</abstract>
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    <location>
        <url>https://aclanthology.org/W19-6134</url>
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    <part>
        <date>2019-sep–oct</date>
        <extent unit="page">
            <start>316</start>
            <end>320</end>
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%0 Conference Proceedings
%T Natural Language Processing in Policy Evaluation: Extracting Policy Conditions from IMF Loan Agreements
%A Åkerström, Joakim
%A Daoud, Adel
%A Johansson, Richard
%Y Hartmann, Mareike
%Y Plank, Barbara
%S Proceedings of the 22nd Nordic Conference on Computational Linguistics
%D 2019
%8 sep–oct
%I Linköping University Electronic Press
%C Turku, Finland
%F akerstrom-etal-2019-natural
%X 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.
%U https://aclanthology.org/W19-6134
%P 316-320

Markdown (Informal)

[Natural Language Processing in Policy Evaluation: Extracting Policy Conditions from IMF Loan Agreements](https://aclanthology.org/W19-6134) (Åkerström et al., NoDaLiDa 2019)

• Natural Language Processing in Policy Evaluation: Extracting Policy Conditions from IMF Loan Agreements (Åkerström et al., NoDaLiDa 2019)

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.