The dissertation presents a computational humor detector designed to look at any one of 200 short texts (100 jokes and 100 non-jokes) and to determine whether the text is a joke or not. This is accomplished on the basis of meaning recognition by the computer with the help of an ontology crafted from a children's dictionary, and any additional background knowledge necessary for text understanding. The research is underlaid by an advanced formal semantic theory of humor, and it constitutes the first known attempt to validate a theory of humor computationally. The results of the computational experiments are quite encouraging.
With the advancement of computational technologies, increasingly more emphasis continues to be placed on systems that can handle natural language, whether it involves human-computer communication, or comprehension of written narratives, information on the Web, or human conversations. Humor occurs frequently in verbal communication. Thus, without humor detection no natural language computer system can be considered successful. For full computational understanding of natural language documents and for enabling intelligent conversational agents to handle humor, humor recognition is necessary or at the very least highly desirable.
This exploratory research had to be constrained and its goals narrowed down for the purpose of implementability. The joke detector is therefore restricted to the recognition of short jokes. The domain is further restricted to jokes that are based on ambiguous words, where the detection of several meanings results in humor; and to jokes that are based on similar- or identical-sounding words, where the detection of correct pairs also leads to humor. Because of the meaning-based nature of the research, the system can be extended to other types of humor in text, without changes to the algorithm.
The central hypothesis is that humor recognition of natural language texts is possible when the knowledge needed to comprehend the texts is available in a machine-understandable form. To test the hypothesis, a description logic ontology was built to represent knowledge manifested in natural language texts. The results show that when the information, necessary for humans to understand humor, is available to a machine, it successfully detects humor in text.