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.

翻译：幽默识别在过去几年中已通过不同方法得到广泛研究。然而，现有关于幽默识别的研究并未理解生成幽默的机制。本文受不协调理论启发，认为任何笑话均可分解为两个部分（铺垫与笑点）。这两个部分均具有多种可能的语义，且它们之间存在不协调关系。我们分别使用密度矩阵表示铺垫和笑点的语义不确定性，并借助量子熵设计了QE-Uncertainty和QE-Incongruity作为幽默识别特征。在SemEval2021 Task 7数据集上的实验结果表明，所提出的特征在识别幽默与非幽默文本方面比基线方法更有效。