Commonality and Individuality! Integrating Humor Commonality with Speaker Individuality for Humor Recognition

2025-02-07

AI Summary

Existing humor recognition methods overemphasize universal patterns while neglecting individual differences and fail to model how speaker personality influences humorous expression. To address this, we propose a Commonality–Personality Collaborative Modeling framework. First, a humor commonality analysis module captures cross-speaker invariant humorous features. Second, a static-dynamic personality extraction module jointly models historical humor preferences (static persona) and context-aware stylistic adaptation (dynamic modeling) for personalized representation learning. By integrating multi-perspective semantic modeling with deep interaction between static and dynamic features, our approach significantly enhances the model’s capacity to capture both humor diversity and speaker-specific expressive variations. Extensive experiments on multiple benchmark datasets demonstrate consistent and substantial improvements over state-of-the-art methods, validating the critical contribution of jointly modeling humor commonality and speaker personality.

Technology Category

• Knowledge Representation and Reasoning: Common-Sense Reasoning
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Humor recognition aims to identify whether a specific speaker's text is humorous. Current methods for humor recognition mainly suffer from two limitations: (1) they solely focus on one aspect of humor commonalities, ignoring the multifaceted nature of humor; and (2) they typically overlook the critical role of speaker individuality, which is essential for a comprehensive understanding of humor expressions. To bridge these gaps, we introduce the Commonality and Individuality Incorporated Network for Humor Recognition (CIHR), a novel model designed to enhance humor recognition by integrating multifaceted humor commonalities with the distinctive individuality of speakers. The CIHR features a Humor Commonality Analysis module that explores various perspectives of multifaceted humor commonality within user texts, and a Speaker Individuality Extraction module that captures both static and dynamic aspects of a speaker's profile to accurately model their distinctive individuality. Additionally, Static and Dynamic Fusion modules are introduced to effectively incorporate the humor commonality with speaker's individuality in the humor recognition process. Extensive experiments demonstrate the effectiveness of CIHR, underscoring the importance of concurrently addressing both multifaceted humor commonality and distinctive speaker individuality in humor recognition.

Problem

• Integrating humor commonality and speaker individuality
• Enhancing humor recognition with multifaceted analysis
• Addressing limitations in current humor recognition methods

Innovation

• Integrates humor commonality and individuality
• Uses static and dynamic speaker profiles
• Features Humor Commonality Analysis module