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        <title>Weeding out Conventionalized Metaphors: A Corpus of Novel Metaphor Annotations</title>
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    <name type="personal">
        <namePart type="given">Erik-Lân</namePart>
        <namePart type="family">Do Dinh</namePart>
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        <namePart type="given">Hannah</namePart>
        <namePart type="family">Wieland</namePart>
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    <name type="personal">
        <namePart type="given">Iryna</namePart>
        <namePart type="family">Gurevych</namePart>
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        <dateIssued>2018-oct-nov</dateIssued>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Chiang</namePart>
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            <namePart type="family">Hockenmaier</namePart>
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            <namePart type="given">Jun’ichi</namePart>
            <namePart type="family">Tsujii</namePart>
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    <abstract>We encounter metaphors every day, but only a few jump out on us and make us stumble. However, little effort has been devoted to investigating more novel metaphors in comparison to general metaphor detection efforts. We attribute this gap primarily to the lack of larger datasets that distinguish between conventionalized, i.e., very common, and novel metaphors. The goal of this paper is to alleviate this situation by introducing a crowdsourced novel metaphor annotation layer for an existing metaphor corpus. Further, we analyze our corpus and investigate correlations between novelty and features that are typically used in metaphor detection, such as concreteness ratings and more semantic features like the Potential for Metaphoricity. Finally, we present a baseline approach to assess novelty in metaphors based on our annotations.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1171</identifier>
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        <date>2018-oct-nov</date>
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%0 Conference Proceedings
%T Weeding out Conventionalized Metaphors: A Corpus of Novel Metaphor Annotations
%A Do Dinh, Erik-Lân
%A Wieland, Hannah
%A Gurevych, Iryna
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F do-dinh-etal-2018-weeding
%X We encounter metaphors every day, but only a few jump out on us and make us stumble. However, little effort has been devoted to investigating more novel metaphors in comparison to general metaphor detection efforts. We attribute this gap primarily to the lack of larger datasets that distinguish between conventionalized, i.e., very common, and novel metaphors. The goal of this paper is to alleviate this situation by introducing a crowdsourced novel metaphor annotation layer for an existing metaphor corpus. Further, we analyze our corpus and investigate correlations between novelty and features that are typically used in metaphor detection, such as concreteness ratings and more semantic features like the Potential for Metaphoricity. Finally, we present a baseline approach to assess novelty in metaphors based on our annotations.
%R 10.18653/v1/D18-1171
%U https://aclanthology.org/D18-1171
%U https://doi.org/10.18653/v1/D18-1171
%P 1412-1424

Markdown (Informal)

[Weeding out Conventionalized Metaphors: A Corpus of Novel Metaphor Annotations](https://aclanthology.org/D18-1171) (Do Dinh et al., EMNLP 2018)

• Weeding out Conventionalized Metaphors: A Corpus of Novel Metaphor Annotations (Do Dinh et al., EMNLP 2018)

ACL

• Erik-Lân Do Dinh, Hannah Wieland, and Iryna Gurevych. 2018. Weeding out Conventionalized Metaphors: A Corpus of Novel Metaphor Annotations. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1412–1424, Brussels, Belgium. Association for Computational Linguistics.