Mining Effective Features Using Quantum Entropy for Humor Recognition

TL;DR

This paper introduces a novel humor recognition method using quantum entropy to model semantic uncertainty and incongruity between joke components, outperforming baselines on a standard dataset.

Contribution

It proposes a new quantum entropy-based feature extraction approach for humor recognition, inspired by incongruity theory, capturing semantic uncertainty in joke components.

Findings

Quantum entropy features outperform baselines in humor detection.

The method effectively models semantic incongruity in jokes.

Experimental results validate the approach on SemEval2021 dataset.

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.