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    <titleInfo>
        <title>Paraphrase to Explicate: Revealing Implicit Noun-Compound Relations</title>
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    <name type="personal">
        <namePart type="given">Vered</namePart>
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            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
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        <name type="personal">
            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
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        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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    <abstract>Revealing the implicit semantic relation between the constituents of a noun-compound is important for many NLP applications. It has been addressed in the literature either as a classification task to a set of pre-defined relations or by producing free text paraphrases explicating the relations. Most existing paraphrasing methods lack the ability to generalize, and have a hard time interpreting infrequent or new noun-compounds. We propose a neural model that generalizes better by representing paraphrases in a continuous space, generalizing for both unseen noun-compounds and rare paraphrases. Our model helps improving performance on both the noun-compound paraphrasing and classification tasks.</abstract>
    <identifier type="citekey">shwartz-dagan-2018-paraphrase</identifier>
    <identifier type="doi">10.18653/v1/P18-1111</identifier>
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%0 Conference Proceedings
%T Paraphrase to Explicate: Revealing Implicit Noun-Compound Relations
%A Shwartz, Vered
%A Dagan, Ido
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F shwartz-dagan-2018-paraphrase
%X Revealing the implicit semantic relation between the constituents of a noun-compound is important for many NLP applications. It has been addressed in the literature either as a classification task to a set of pre-defined relations or by producing free text paraphrases explicating the relations. Most existing paraphrasing methods lack the ability to generalize, and have a hard time interpreting infrequent or new noun-compounds. We propose a neural model that generalizes better by representing paraphrases in a continuous space, generalizing for both unseen noun-compounds and rare paraphrases. Our model helps improving performance on both the noun-compound paraphrasing and classification tasks.
%R 10.18653/v1/P18-1111
%U https://aclanthology.org/P18-1111
%U https://doi.org/10.18653/v1/P18-1111
%P 1200-1211

Markdown (Informal)

[Paraphrase to Explicate: Revealing Implicit Noun-Compound Relations](https://aclanthology.org/P18-1111) (Shwartz & Dagan, ACL 2018)

• Paraphrase to Explicate: Revealing Implicit Noun-Compound Relations (Shwartz & Dagan, ACL 2018)

ACL

• Vered Shwartz and Ido Dagan. 2018. Paraphrase to Explicate: Revealing Implicit Noun-Compound Relations. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1200–1211, Melbourne, Australia. Association for Computational Linguistics.