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.

该研究基于不相容性理论，将笑话分成设定和反讽两个组成部分，使用密度矩阵来表示设定和反讽的语义不确定性，通过量子熵设计了 QE-Uncertainty 和 QE-Incongruity 作为幽默识别的特征，并在 SemEval2021 任务 7 数据集上实现了更加准确的幽默识别。

利用量子熵挖掘有效特征进行幽默识别

Mining Effective Features Using Quantum Entropy for Humor Recognition

Humor recognition has been widely studied as a text classification problem
using data-driven approaches. However, most existing work does not examine the
actual joke mechanism to understand humor. We break down any joke into two
distinct components: the set-up and the punchline, and further explore the
special relationship between them. Inspired by the incongruity theory of humor,
we model the set-up as the part developing semantic uncertainty, and the
punchline disrupting audience expectations. With increasingly powerful language
models, we were able to feed the set-up along with the punchline into the GPT-2
language model, and calculate the uncertainty and surprisal values of the
jokes. By conducting experiments on the SemEval 2021 Task 7 dataset, we found
that these two features have better capabilities of telling jokes from
non-jokes, compared with existing baselines.

通过研究幽默机制，将笑话分成两个部分：设定和妙语。由于底层模型的不断升级，我们能够将设置和妙语一起馈入 GPT-2 语言模型，并计算笑话的不确定性和惊喜度。实验结果表明，这两个特征具有更好的识别非笑话和笑话的能力。