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        <title>Assessing the Difficulty of Classifying ConceptNet Relations in a Multi-Label Classification Setting</title>
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    <name type="personal">
        <namePart type="given">Maria</namePart>
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    <name type="personal">
        <namePart type="given">Michael</namePart>
        <namePart type="family">Staniek</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Vivi</namePart>
        <namePart type="family">Nastase</namePart>
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    <name type="personal">
        <namePart type="given">Anette</namePart>
        <namePart type="family">Frank</namePart>
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            <title>RELATIONS - Workshop on meaning relations between phrases and sentences</title>
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        <name type="personal">
            <namePart type="given">Venelin</namePart>
            <namePart type="family">Kovatchev</namePart>
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        <name type="personal">
            <namePart type="given">Darina</namePart>
            <namePart type="family">Gold</namePart>
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            <namePart type="given">Torsten</namePart>
            <namePart type="family">Zesch</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Commonsense knowledge relations are crucial for advanced NLU tasks. We examine the learnability of such relations as represented in ConceptNet, taking into account their specific properties, which can make relation classification difficult: a given concept pair can be linked by multiple relation types, and relations can have multi-word arguments of diverse semantic types. We explore a neural open world multi-label classification approach that focuses on the evaluation of classification accuracy for individual relations. Based on an in-depth study of the specific properties of the ConceptNet resource, we investigate the impact of different relation representations and model variations. Our analysis reveals that the complexity of argument types and relation ambiguity are the most important challenges to address. We design a customized evaluation method to address the incompleteness of the resource that can be expanded in future work.</abstract>
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    <identifier type="doi">10.18653/v1/W19-0801</identifier>
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%0 Conference Proceedings
%T Assessing the Difficulty of Classifying ConceptNet Relations in a Multi-Label Classification Setting
%A Becker, Maria
%A Staniek, Michael
%A Nastase, Vivi
%A Frank, Anette
%Y Kovatchev, Venelin
%Y Gold, Darina
%Y Zesch, Torsten
%S RELATIONS - Workshop on meaning relations between phrases and sentences
%D 2019
%8 May
%I Association for Computational Linguistics
%C Gothenburg, Sweden
%F becker-etal-2019-assessing
%X Commonsense knowledge relations are crucial for advanced NLU tasks. We examine the learnability of such relations as represented in ConceptNet, taking into account their specific properties, which can make relation classification difficult: a given concept pair can be linked by multiple relation types, and relations can have multi-word arguments of diverse semantic types. We explore a neural open world multi-label classification approach that focuses on the evaluation of classification accuracy for individual relations. Based on an in-depth study of the specific properties of the ConceptNet resource, we investigate the impact of different relation representations and model variations. Our analysis reveals that the complexity of argument types and relation ambiguity are the most important challenges to address. We design a customized evaluation method to address the incompleteness of the resource that can be expanded in future work.
%R 10.18653/v1/W19-0801
%U https://aclanthology.org/W19-0801
%U https://doi.org/10.18653/v1/W19-0801

Markdown (Informal)

[Assessing the Difficulty of Classifying ConceptNet Relations in a Multi-Label Classification Setting](https://aclanthology.org/W19-0801) (Becker et al., IWCS 2019)

• Assessing the Difficulty of Classifying ConceptNet Relations in a Multi-Label Classification Setting (Becker et al., IWCS 2019)

ACL

• Maria Becker, Michael Staniek, Vivi Nastase, and Anette Frank. 2019. Assessing the Difficulty of Classifying ConceptNet Relations in a Multi-Label Classification Setting. In RELATIONS - Workshop on meaning relations between phrases and sentences, Gothenburg, Sweden. Association for Computational Linguistics.