Humor and Comedy

Through most of the twentieth century, psychologists were the preeminent theorists of humor. Since the late twentieth century, linguists, neuroscientists, and computer scientists have also addressed the subject. This chapter presents classic theories of humor—relief/arousal, superiority/disparagement, and incongruity theories, including recent neuroimaging research—followed by an overview of linguistic and semantic theories. The field of computational humor is described, including humor during human–artificial intelligence interaction. The uses and effects of humor are summarized in the areas of education, advertising, and health. Although humor and laughter may not always improve learning, persuasion, or physical health they can enhance the credibility of the communicator and improve the quality of life.

Reverse-Engineering Satire, or “Paper on Computational Humor Accepted despite Making Serious Advances”

Humor is an essential human trait. Efforts to understand humor have called out links between humor and the foundations of cognition, as well as the importance of humor in social engagement. As such, it is a promising and important subject of study, with relevance for artificial intelligence and human– computer interaction. Previous computational work on humor has mostly operated at a coarse level of granularity, e.g., predicting whether an entire sentence, paragraph, document, etc., is humorous. As a step toward deep understanding of humor, we seek fine-grained models of attributes that make a given text humorous. Starting from the observation that satirical news headlines tend to resemble serious news headlines, we build and analyze a corpus of satirical headlines paired with nearly identical but serious headlines. The corpus is constructed via Unfun.me, an online game that incentivizes players to make minimal edits to satirical headlines with the goal of making other players believe the results are serious headlines. The edit operations used to successfully remove humor pinpoint the words and concepts that play a key role in making the original, satirical headline funny. Our analysis reveals that the humor tends to reside toward the end of headlines, and primarily in noun phrases, and that most satirical headlines follow a certain logical pattern, which we term false analogy. Overall, this paper deepens our understanding of the syntactic and semantic structure of satirical news headlines and provides insights for building humor-producing systems.

Computational humor and Christie Davies’ basis for joke comparison

While historically computational humor paid very little attention to sociology and mostly took into account subparts of linguistics and some psychology, Christie Davies wrote a number of papers that should affect the study of computational humor directly. This paper will look at one paper to illustrate this point, namely Christie’s chapter in the Primer of Humor Research. With the advancements in computational processing and big data analysis/analytics, it is becoming possible to look at a large collection of humorous texts that are available on the web. In particular, older texts, including joke materials, that are being scanned from previously published printed versions. Most of the approaches within computational humor concentrated on comparison of present/existing jokes, without taking into account classes of jokes that are absent in a given setting. While the absence of a class is unlikely to affect classification – something that researchers in computational humor seem to be interested in – it does come into light when features of various classes are compared and conclusions are being made. This paper will describe existing approaches and how they could be enhanced, thanks to Davies’s contributions and the advancements in data processing.

Towards the Cognitive Informatics of Natural Language

This paper deals with a contribution of computational analysis of verbal humor to natural language cognition. After a brief introduction to the growing area of computational humor and of its roots in humor theories, it describes and compares the results of a human-subject and computer experiment. The specific interest is to compare how well the computer, equipped with the resources and methodologies of the Ontological Semantic Technology, a comprehensive meaning access approach to natural language processing, can model several aspects of the cognitive behaviors of humans processing jokes from the Internet. The paper, sharing several important premises with cognitive informatics, is meant as a direct contribution to this rapidly developing transdisciplinary field, and as such, it bears on cognitive computing as well, especially at the level of implementation of computational humor in non-toy systems and the relationship to human cognitive processes of understanding and producing humor.

Can Computers Create Humor?

Despite the fact that AI has always been adventurous in trying to elucidate complex aspects of human behaviour, only recently has there been research into computational modelling of humor. One obstacle to progress is the lack of a precise and detailed theory of how humor operates. Nevertheless, since the early 1990s, there have been a number of small programs that create simple verbal humor, and more recently there have been studies of the automatic classification of the humorous status of texts. In addition, there are a number of advocates of the practical uses of computational humor: in user-interfaces, in education, and in advertising. Computer-generated humor is still quite basic, but it could be viewed as a form of exploratory creativity. For computational humor to improve, some hard problems in AI will have to be addressed.