# Mining Effective Features Using Quantum Entropy for Humor Recognition

arXiv: https://arxiv.org/abs/2302.03716 · HTML: https://mlsanity.com/paper/2302.03716/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 7 Feb 2023.

## Builds on (most cited first, 3 of 3)

- [Humor Detection: A Transformer Gets the Last Laugh](https://mlsanity.com/paper/1909.00252/) (2019)
- [Uncertainty and Surprisal Jointly Deliver the Punchline: Exploiting Incongruity-Based Features for Humor Recognition](https://mlsanity.com/paper/2012.12007/) (2020)
- [Predicting Audience's Laughter Using Convolutional Neural Network](https://mlsanity.com/paper/1702.02584/) (2017)

## Built on by (most cited first, 4 of 4)

- [Robust Evaluation Measures for Evaluating Social Biases in Masked Language Models](https://mlsanity.com/paper/2401.11601/) (2024)
- [Quantifying Stereotypes in Language](https://mlsanity.com/paper/2401.15535/) (2024)
- [Revealing the impact of synthetic native samples and multi-tasking strategies in Hindi-English code-mixed humour and sarcasm detection](https://mlsanity.com/paper/2412.12761/) (2024)
- [Sentence-Level Contextual Entrainment in Large Language Models](https://mlsanity.com/paper/2606.24077/) (2026)

## Abstract (arXiv)

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.