Phonetic Cues Improve LLM-Based Pun Detection in Short Text

2026-06-27 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A study by Adith Santosh Thaniserikaran and Govind Harikrishnan explores joke detection in short text, specifically targeting puns that arise from lexical ambiguity. Building on Attardo and Raskin's theory, which posits humor from script opposition via mechanisms like homography or homophony, their framework combines contextual semantic analysis for homographs with phoneme-level similarity for homophones and near-homophones. This novel approach leverages tools such as CMUdict, weighted Levenshtein distance, and prompt-based reasoning to uncover linguistic ambiguities not immediately visible through spelling alone. The research conclusively shows that integrating explicit phonetic modeling substantially improves the accuracy of detecting sound-based puns in short textual content.

Key takeaway

For NLP Engineers developing humor detection systems or content analysis tools, incorporating explicit phonetic modeling is crucial. Your models, especially those relying on LLMs, will significantly improve their ability to identify sound-based puns by integrating resources like CMUdict and weighted Levenshtein distance. This approach helps uncover lexical ambiguities not evident from spelling alone, leading to more nuanced and accurate humor recognition.

Key insights

Explicit phonetic modeling significantly improves LLM-based detection of sound-based puns by revealing hidden lexical ambiguities.

Principles

• Humor stems from script opposition.
• Lexical ambiguity drives pun humor.
• Homophony and homography are key mechanisms.

Method

The method integrates contextual semantic analysis for homographs with phoneme-level similarity for homophones and near-homophones, employing CMUdict, weighted Levenshtein distance, and prompt-based reasoning to reveal hidden ambiguities.

In practice

• Enhance LLM pun detection.
• Apply CMUdict for phoneme data.
• Use Levenshtein for sound similarity.

Topics

• Pun Detection
• Lexical Ambiguity
• Phonetic Modeling
• Large Language Models
• CMUdict
• Levenshtein Distance

Best for: Research Scientist, AI Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.