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        <title>Mining Effective Features Using Quantum Entropy for Humor Recognition</title>
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            <namePart type="given">Andreas</namePart>
            <namePart type="family">Vlachos</namePart>
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    <abstract>Humor recognition has been extensively studied with different methods in the past years. However, existing studies on humor recognition do not understand the mechanisms that generate humor. In this paper, inspired by the incongruity theory, any joke can be divided into two components (the setup and the punchline). Both components have multiple possible semantics, and there is an incongruous relationship between them. We use density matrices to represent the semantic uncertainty of the setup and the punchline, respectively, and design QE-Uncertainty and QE-Incongruity with the help of quantum entropy as features for humor recognition. The experimental results on the SemEval2021 Task 7 dataset show that the proposed features are more effective than the baselines for recognizing humorous and non-humorous texts.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-eacl.152</identifier>
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        <url>https://aclanthology.org/2023.findings-eacl.152</url>
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        <date>2023-05</date>
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            <start>2048</start>
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%0 Conference Proceedings
%T Mining Effective Features Using Quantum Entropy for Humor Recognition
%A Liu, Yang
%A Hou, Yuexian
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F liu-hou-2023-mining
%X Humor recognition has been extensively studied with different methods in the past years. However, existing studies on humor recognition do not understand the mechanisms that generate humor. In this paper, inspired by the incongruity theory, any joke can be divided into two components (the setup and the punchline). Both components have multiple possible semantics, and there is an incongruous relationship between them. We use density matrices to represent the semantic uncertainty of the setup and the punchline, respectively, and design QE-Uncertainty and QE-Incongruity with the help of quantum entropy as features for humor recognition. The experimental results on the SemEval2021 Task 7 dataset show that the proposed features are more effective than the baselines for recognizing humorous and non-humorous texts.
%R 10.18653/v1/2023.findings-eacl.152
%U https://aclanthology.org/2023.findings-eacl.152
%U https://doi.org/10.18653/v1/2023.findings-eacl.152
%P 2048-2053

Markdown (Informal)

[Mining Effective Features Using Quantum Entropy for Humor Recognition](https://aclanthology.org/2023.findings-eacl.152) (Liu & Hou, Findings 2023)

• Mining Effective Features Using Quantum Entropy for Humor Recognition (Liu & Hou, Findings 2023)

ACL

• Yang Liu and Yuexian Hou. 2023. Mining Effective Features Using Quantum Entropy for Humor Recognition. In Findings of the Association for Computational Linguistics: EACL 2023, pages 2048–2053, Dubrovnik, Croatia. Association for Computational Linguistics.