Mining Effective Features Using Quantum Entropy for Humor Recognition (2023)

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.

## introduction humor is one of the most distinctive features of human behavior and a sign of mental maturity @xcite . the study of humor has received extensive attention in the fields of linguistics, philosophy, psychology, and sociology @xcite . computational humor is of particular interest, with the potential to transform computers into creative and motivational tools @xcite . this paper restricts research to humor recognition in computational humor, which aims to recognize whether a piece of text is humorous. as shown in figure 1 , a joke usually includes two components: the setup and the punchline. the reader generates an expectation of the following text (the punchline) based on the content of the setup, and if the following text violates the reader's expectation, humor is generated, and vice versa. in fact, the incongruity theory of humor can explain the above process of producing humor. the incongruity theory states that humor is generated because a thing (the setup) has multiple underlying concepts, and there is an incongruity between the concept involved in the situation and the real object it represents (the punchline). features based on semantic similarity @xcite and word association @xcite have achieved certain results, but they lack consideration of humorous mechanisms. @xcite calculated the uncertainty and the surprisal values of the joke with the help of the gpt-2. but they did not model the semantic incongruity between the setup and the punchline. while the above approaches are somewhat effective, the incongruity theory requires us to model semantic uncertainty and the incongruity between the setup and the punchline. we take inspiration from quantum theory and use density matrices to represent the uncertainty of text semantics. specifically, the setup and the punchline are represented as density matrices, respectively. then, take the quantum entropy of the setup as quantum entropy uncertainty (qe-uncertainty) and the conditional quantum entropy between the setup and the punchline as quantum entropy incongruity (qe-incongruity). experiments conducted on a manually-labeled dataset demonstrate that these two features are better than existing baselines in distinguishing between humorous and non-humorous texts, confirming the necessity of correlating semantic uncertainty with quantum theory. ## methodology the incongruity theory holds that the prerequisite for humor is that the text has multiple semantic aspects. the reader does not understand one meaning but expects one while the punchline provides another, leading to incongruity. according to the incongruity theory, we should design features to represent the multiple semantic overlaps of the setup, as well as the incongruity of the semantics of the setup and the punchline. normalize each word w @xmath0: ## related work the existing text humor recognition methods are mainly divided into feature-based methods and deep learning-based methods. mihalcea and strapparava (2005) use automatic classification techniques to integrate humor-specific features (alliteration, antonymy, slang) and content-based features into a machine-learning framework for humor classification tasks. mihalcea et al. (2010) divide the humor text into two components: the setup and the punchline. humor recognition is performed by calculating the semantic correlation between the setup and the punchline based on the incongruity theory. morales and zhai (2017) use a generative language model combined with background text resources to construct multiple features to identify whether a comment is a humorous text. liu et al. (2018) combine discourse analysis and sentiment analysis to extract sentiment-related features to address humor recognition. xie et al. (2021) developed uncertainty and superisal with the help of the prediction results of the pre-trained language model gpt-2. in recent years, with the development of deep learning, some deep learning-based methods have been proposed. chen and lee (2017) use convolutional neural networks to identify humor in the ted talks corpus. chen and soo (2018) used the highway network architecture to implement deep convolutional neural networks to predict humor on datasets of different types and different languages. weller and seppi (2019) used pre-trained bert for the humor classification task. fan et al. (2020) combine the bi-gru network with phonetic structure and ambiguity for humor recognition. ## conclusion in this paper, we model semantic uncertainty with a quantum framework. inspired by the incongruity theory, we design two features, qe-uncertainty and qe-incongruity. we conduct experiments on the humor dataset, and the experimental results demonstrate the effectiveness of our proposed features. this suggests that the density matrix is an excellent framework for describing uncertainty and that the quantum entropy of the density matrix is a better feature to distinguish jokes from non-jokes than previously proposed features. we believe that the quantum framework can also be used for semantic uncertainty modeling for other tasks in the future.