Tightening up Joke Structure: Not by Length Alone.

Tightening up Joke Structure: Not by Length Alone Christian F. Hempelmann (chempelm@purdue.edu) CERIAS, Purdue University 656 Oval Dr., West Lafayette, IN 47907-2086 USA Julia M. Taylor (jtaylor1@purdue.edu) Department of Computer and Information Technology, Purdue University 401 N. Grant St., West Lafayette, IN 47907-2021 USA Victor Raskin (vraskin@purdue.edu) CERIAS, Purdue University 656 Oval Dr., West Lafayette, IN 47907-2086 USA Abstract by either participant. The first scenario enables the computer to generate humor in reaction to a human cue in human- computer interaction, the second scenario lets the computer identify humor in the same scenario and enables it to react competently to it. Most existing theories available for humor detection or generation fall short of providing the adequate support for this task. These theories are either too fine-grained to be useful or too coarse to correctly classify any given text as a joke or a non-joke. But our ontological-semantic system provides a sufficiently rich and flexible basis because it operates at the level of human text-meaning processing. In the following, we will summarize the state of the art, introduce our approach, and then discuss a pilot study assessing human appreciation of jokes in variants of different length and types of manipulation. The paper seeks to tighten up the notion of joke structure in the context of the Ontological Semantic Theory of Humor for computational use. The method is testing the prior hypothesis that a minimalist version of a joke, consisting only of the setup and punch line, is the most effective one. A small ‘human computation’ pilot study casts serious doubt on this hypothesis. Keywords: humor research; (minimalist) joke structure; setup; punch line; joke versions. 1. Introduction The human ability to communicate is incomplete without humor. If a computational system is ever to approximate this human communicative ability and act as a competent partner in a conversation with a human, humor must be accounted for. Over the last decades, humor research has become an intense multidisciplinary effort with significant contributions from linguistics, psychology, sociology, neuro- and cognitive sciences (Raskin 1985, 2008; Ruch 1998, Oring 1992, Davies 1990, Attardo 1994. Morreall 1983). Along with theories and analyses of human- generated and perceived humor, since the early 1990s, there have been more explorations of computational humor as well, starting with attempts at humor generation through humor detection to semantically based systems (see Section 2.1 below). Part of the difficulty in relating computational humor- generation and, to a lesser degree, humor-detection systems to human appreciation is the question how much information has to be present in the text of a joke to ensure a successful setup and the most effective punch line. This is precisely what has not been addressed yet on the computational front—how much information is enough and not too much to carry a joke without risking the opposite extremes of crypticality or verbosity. Two related goals of this line of research are a) to create an NLP system capable of understanding the mechanism of a joke at a level sufficient for providing a punch line to a human-generated setup (even if unintentionally) and b), conversely, for the computer to react competently to a human-generated punch line that follows a setup, generated 2. Background 2.1. State of the Art The usefulness of and motivations for computational humor, along with its feasibility, have been intensely discussed (see Ritchie 2004, Hempelmann 2008, Taylor 2008, Strapparava et al. 2011 and references in all of these sources). The most useful work on computational humor is based on a humor theory and seeks to gain further insights, to validate, and to improve the theory, while taking advantage of its assets. Work on humor theories has a long history, and, to this day, the true multifaceted nature of humor is still being debated (Raskin 1985, Morreall 1983, Oring 1992, Ruch 1998, Davies 1990, Attardo 1994): there is no universally accepted theory of humor that explains “what is funny, why it is funny, how it is funny, when it is funny, and to whom it is funny.” (Raskin 1985: 5). The linguistic theories of humor (Raskin 1985, Attardo & Raskin 1991) have reached a level of formal representation that is adaptable for the computation of any humorous text (Raskin et al. 2009a,b). But the best-known and most-used linguistic theory of humor remains the early Script-based Semantic Theory of Humor (SSTH: Raskin 1985).