Phonetic Cues Improve LLM-Based Pun Detection in Short Text

Adith Santosh Thaniserikaran; Govind Harikrishnan

Phonetic Cues Improve LLM-Based Pun Detection in Short Text

Adith Santosh Thaniserikaran, Govind Harikrishnan

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Abstract

This paper studies joke detection in short text, focusing only on jokes triggered by lexical ambiguity. Following Attardo and Raskin, we treat these jokes as cases where humor arises from a script opposition activated through a logical mechanism such as homography or homophony. Our framework combines contextuals emantic analysis for homographs with phoneme-level similarity for homophones and near-homophones, using CMUdict, weighted Levenshtein distance, and prompt-based reasoning to recover ambiguities that are not visible in spelling alone. Results show that explicit phonetic modeling improves detection of sound-based puns.

Anthology ID:: 2026.chum-1.6
Volume:: Proceedings of the 2nd Workshop on Computational Humor (CHum 2026)
Month:: July
Year:: 2026
Address:: Online
Editors:: Ori Amir, Christian F. Hempelmann, Julia Rayz, Tiansi Dong, Tristan Miller
Venues:: chum | WS
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 72–80
Language:
URL:: https://aclanthology.org/2026.chum-1.6/
DOI:
Bibkey:
Cite (ACL):: Adith Santosh Thaniserikaran and Govind Harikrishnan. 2026. Phonetic Cues Improve LLM-Based Pun Detection in Short Text. In Proceedings of the 2nd Workshop on Computational Humor (CHum 2026), pages 72–80, Online. Association for Computational Linguistics.
Cite (Informal):: Phonetic Cues Improve LLM-Based Pun Detection in Short Text (Thaniserikaran & Harikrishnan, chum 2026)
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PDF:: https://aclanthology.org/2026.chum-1.6.pdf

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@inproceedings{thaniserikaran-harikrishnan-2026-phonetic,
    title = "Phonetic Cues Improve {LLM}-Based Pun Detection in Short Text",
    author = "Thaniserikaran, Adith Santosh  and
      Harikrishnan, Govind",
    editor = "Amir, Ori  and
      Hempelmann, Christian F.  and
      Rayz, Julia  and
      Dong, Tiansi  and
      Miller, Tristan",
    booktitle = "Proceedings of the 2nd Workshop on Computational Humor ({CH}um 2026)",
    month = jul,
    year = "2026",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.chum-1.6/",
    pages = "72--80",
    ISBN = "979-8-89176-431-6",
    abstract = "This paper studies joke detection in short text, focusing only on jokes triggered by lexical ambiguity. Following Attardo and Raskin, we treat these jokes as cases where humor arises from a script opposition activated through a logical mechanism such as homography or homophony. Our framework combines contextuals emantic analysis for homographs with phoneme-level similarity for homophones and near-homophones, using CMUdict, weighted Levenshtein distance, and prompt-based reasoning to recover ambiguities that are not visible in spelling alone. Results show that explicit phonetic modeling improves detection of sound-based puns."
}

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    <abstract>This paper studies joke detection in short text, focusing only on jokes triggered by lexical ambiguity. Following Attardo and Raskin, we treat these jokes as cases where humor arises from a script opposition activated through a logical mechanism such as homography or homophony. Our framework combines contextuals emantic analysis for homographs with phoneme-level similarity for homophones and near-homophones, using CMUdict, weighted Levenshtein distance, and prompt-based reasoning to recover ambiguities that are not visible in spelling alone. Results show that explicit phonetic modeling improves detection of sound-based puns.</abstract>
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%0 Conference Proceedings
%T Phonetic Cues Improve LLM-Based Pun Detection in Short Text
%A Thaniserikaran, Adith Santosh
%A Harikrishnan, Govind
%Y Amir, Ori
%Y Hempelmann, Christian F.
%Y Rayz, Julia
%Y Dong, Tiansi
%Y Miller, Tristan
%S Proceedings of the 2nd Workshop on Computational Humor (CHum 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C Online
%@ 979-8-89176-431-6
%F thaniserikaran-harikrishnan-2026-phonetic
%X This paper studies joke detection in short text, focusing only on jokes triggered by lexical ambiguity. Following Attardo and Raskin, we treat these jokes as cases where humor arises from a script opposition activated through a logical mechanism such as homography or homophony. Our framework combines contextuals emantic analysis for homographs with phoneme-level similarity for homophones and near-homophones, using CMUdict, weighted Levenshtein distance, and prompt-based reasoning to recover ambiguities that are not visible in spelling alone. Results show that explicit phonetic modeling improves detection of sound-based puns.
%U https://aclanthology.org/2026.chum-1.6/
%P 72-80

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Markdown (Informal)

[Phonetic Cues Improve LLM-Based Pun Detection in Short Text](https://aclanthology.org/2026.chum-1.6/) (Thaniserikaran & Harikrishnan, chum 2026)

Phonetic Cues Improve LLM-Based Pun Detection in Short Text (Thaniserikaran & Harikrishnan, chum 2026)

ACL

Adith Santosh Thaniserikaran and Govind Harikrishnan. 2026. Phonetic Cues Improve LLM-Based Pun Detection in Short Text. In Proceedings of the 2nd Workshop on Computational Humor (CHum 2026), pages 72–80, Online. Association for Computational Linguistics.