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Humor, a fundamental aspect of human communication, poses a formidable challenge for computational systems. Drawing inspiration from the theory of incongruity, we introduce a novel Dual Graph Attention Network-based Feature Extraction Model (DGFEM) tailored specifically for humor recognition. This model constructs a comprehensive relational graph among linguistic tokens, thereby capturing the essence of incongruity that often underlies humorous content. Furthermore, it integrates WordNet for leveraging synonym relationships, enhancing the model’s capacity to identify ambiguity, a key characteristic of humor. Through rigorous experimentation conducted on two benchmark datasets, our DGFEM model has demonstrated remarkable effectiveness in humor recognition, underscoring its potential to advance the state-of-the-art in this domain.

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Humor Recognition Based on Dual Graph Attention Network with Incongruity and Ambiguity Feature Extraction

  • 会议论文

  • 发表日期：

    2025年

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  • Zhichao Ping,
  • Zhi Liu,
  • Mengyi Wang,
  • Hongfei Lin,
  • Yijia Zhang

摘要

Humor, a fundamental aspect of human communication, poses a formidable challenge for computational systems. Drawing inspiration from the theory of incongruity, we introduce a novel Dual Graph Attention Network-based Feature Extraction Model (DGFEM) tailored specifically for humor recognition. This model constructs a comprehensive relational graph among linguistic tokens, thereby capturing the essence of incongruity that often underlies humorous content. Furthermore, it integrates WordNet for leveraging synonym relationships, enhancing the model’s capacity to identify ambiguity, a key characteristic of humor. Through rigorous experimentation conducted on two benchmark datasets, our DGFEM model has demonstrated remarkable effectiveness in humor recognition, underscoring its potential to advance the state-of-the-art in this domain.