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    <titleInfo>
        <title>Exploring Author Context for Detecting Intended vs Perceived Sarcasm</title>
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    <name type="personal">
        <namePart type="given">Silviu</namePart>
        <namePart type="family">Oprea</namePart>
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            <title>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics</title>
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        <name type="personal">
            <namePart type="given">Anna</namePart>
            <namePart type="family">Korhonen</namePart>
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        <name type="personal">
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    <abstract>We investigate the impact of using author context on textual sarcasm detection. We define author context as the embedded representation of their historical posts on Twitter and suggest neural models that extract these representations. We experiment with two tweet datasets, one labelled manually for sarcasm, and the other via tag-based distant supervision. We achieve state-of-the-art performance on the second dataset, but not on the one labelled manually, indicating a difference between intended sarcasm, captured by distant supervision, and perceived sarcasm, captured by manual labelling.</abstract>
    <identifier type="citekey">oprea-magdy-2019-exploring</identifier>
    <identifier type="doi">10.18653/v1/P19-1275</identifier>
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%0 Conference Proceedings
%T Exploring Author Context for Detecting Intended vs Perceived Sarcasm
%A Oprea, Silviu
%A Magdy, Walid
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F oprea-magdy-2019-exploring
%X We investigate the impact of using author context on textual sarcasm detection. We define author context as the embedded representation of their historical posts on Twitter and suggest neural models that extract these representations. We experiment with two tweet datasets, one labelled manually for sarcasm, and the other via tag-based distant supervision. We achieve state-of-the-art performance on the second dataset, but not on the one labelled manually, indicating a difference between intended sarcasm, captured by distant supervision, and perceived sarcasm, captured by manual labelling.
%R 10.18653/v1/P19-1275
%U https://aclanthology.org/P19-1275
%U https://doi.org/10.18653/v1/P19-1275
%P 2854-2859

Markdown (Informal)

[Exploring Author Context for Detecting Intended vs Perceived Sarcasm](https://aclanthology.org/P19-1275) (Oprea & Magdy, ACL 2019)

• Exploring Author Context for Detecting Intended vs Perceived Sarcasm (Oprea & Magdy, ACL 2019)

ACL

• Silviu Oprea and Walid Magdy. 2019. Exploring Author Context for Detecting Intended vs Perceived Sarcasm. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2854–2859, Florence, Italy. Association for Computational Linguistics.