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    <titleInfo>
        <title>MR4AP: Meaning Representation for Application Purposes</title>
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    <name type="personal">
        <namePart type="given">Bastien</namePart>
        <namePart type="family">Giordano</namePart>
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    <name type="personal">
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        <dateIssued>2023-06</dateIssued>
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            <title>Proceedings of the Fourth International Workshop on Designing Meaning Representations</title>
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        <name type="personal">
            <namePart type="given">Julia</namePart>
            <namePart type="family">Bonn</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Nianwen</namePart>
            <namePart type="family">Xue</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Despite the significant progress made in Natural Language Processing (NLP) thanks to deep learning techniques, efforts are still needed to model explicit, factual, and accurate meaning representation formalisms. In this article, we present a comparative table of ten formalisms that have been proposed over the last thirty years, and we describe and put forth our own, Meaning Representation for Application Purposes (MR4AP), developed in an industrial context with a definitive applicative aim.</abstract>
    <identifier type="citekey">giordano-lopez-2023-mr4ap</identifier>
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        <url>https://aclanthology.org/2023.dmr-1.11</url>
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        <date>2023-06</date>
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            <start>110</start>
            <end>121</end>
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%0 Conference Proceedings
%T MR4AP: Meaning Representation for Application Purposes
%A Giordano, Bastien
%A Lopez, Cédric
%Y Bonn, Julia
%Y Xue, Nianwen
%S Proceedings of the Fourth International Workshop on Designing Meaning Representations
%D 2023
%8 June
%I Association for Computational Linguistics
%C Nancy, France
%F giordano-lopez-2023-mr4ap
%X Despite the significant progress made in Natural Language Processing (NLP) thanks to deep learning techniques, efforts are still needed to model explicit, factual, and accurate meaning representation formalisms. In this article, we present a comparative table of ten formalisms that have been proposed over the last thirty years, and we describe and put forth our own, Meaning Representation for Application Purposes (MR4AP), developed in an industrial context with a definitive applicative aim.
%U https://aclanthology.org/2023.dmr-1.11
%P 110-121

Markdown (Informal)

[MR4AP: Meaning Representation for Application Purposes](https://aclanthology.org/2023.dmr-1.11) (Giordano & Lopez, DMR-WS 2023)

• MR4AP: Meaning Representation for Application Purposes (Giordano & Lopez, DMR-WS 2023)

ACL

• Bastien Giordano and Cédric Lopez. 2023. MR4AP: Meaning Representation for Application Purposes. In Proceedings of the Fourth International Workshop on Designing Meaning Representations, pages 110–121, Nancy, France. Association for Computational Linguistics.