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        <title>A deep-learning framework to detect sarcasm targets</title>
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        <namePart type="given">Jasabanta</namePart>
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        <namePart type="given">Srijan</namePart>
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    <name type="personal">
        <namePart type="given">Animesh</namePart>
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            <title>Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)</title>
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            <namePart type="given">Jing</namePart>
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    <abstract>In this paper we propose a deep learning framework for sarcasm target detection in predefined sarcastic texts. Identification of sarcasm targets can help in many core natural language processing tasks such as aspect based sentiment analysis, opinion mining etc. To begin with, we perform an empirical study of the socio-linguistic features and identify those that are statistically significant in indicating sarcasm targets (p-values in the range(0.05,0.001)). Finally, we present a deep-learning framework augmented with socio-linguistic features to detect sarcasm targets in sarcastic book-snippets and tweets. We achieve a huge improvement in the performance in terms of exact match and dice scores compared to the current state-of-the-art baseline.</abstract>
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    <identifier type="doi">10.18653/v1/D19-1663</identifier>
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%0 Conference Proceedings
%T A deep-learning framework to detect sarcasm targets
%A Patro, Jasabanta
%A Bansal, Srijan
%A Mukherjee, Animesh
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F patro-etal-2019-deep
%X In this paper we propose a deep learning framework for sarcasm target detection in predefined sarcastic texts. Identification of sarcasm targets can help in many core natural language processing tasks such as aspect based sentiment analysis, opinion mining etc. To begin with, we perform an empirical study of the socio-linguistic features and identify those that are statistically significant in indicating sarcasm targets (p-values in the range(0.05,0.001)). Finally, we present a deep-learning framework augmented with socio-linguistic features to detect sarcasm targets in sarcastic book-snippets and tweets. We achieve a huge improvement in the performance in terms of exact match and dice scores compared to the current state-of-the-art baseline.
%R 10.18653/v1/D19-1663
%U https://aclanthology.org/D19-1663
%U https://doi.org/10.18653/v1/D19-1663
%P 6336-6342

Markdown (Informal)

[A deep-learning framework to detect sarcasm targets](https://aclanthology.org/D19-1663) (Patro et al., EMNLP-IJCNLP 2019)

• A deep-learning framework to detect sarcasm targets (Patro et al., EMNLP-IJCNLP 2019)

ACL

• Jasabanta Patro, Srijan Bansal, and Animesh Mukherjee. 2019. A deep-learning framework to detect sarcasm targets. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6336–6342, Hong Kong, China. Association for Computational Linguistics.