Testing Humor Theory Using Word and Sentence Embeddings

AbstractA basic prediction of incongruity theory is that semantic scripts in verbal humor should be in a state of incongruity. We test this prediction using a dataset of 1,182 word/phrase pairs extracted from a set of imperfect puns. Incongruity was defined as the cosine distance between their word vector representations. We compare these pun distances against similarity metrics for the pun words against their synonyms, extracted from WordNet. Results indicate a significantly lower degree of similarity between pun words when compared to their synonyms. Our findings support the basic predictions of incongruity theory and provide computational researchers with a baseline metric to model humorous incongruity.