  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.

Computation and Language