Abstract

N atural language's creative genres are traditionally considered to be outside the scope of computational modeling. Computational linguists have paid little attention to humor in particular because it is puzzling by nature. However, given the importance of humor in our daily lives and computers in our work and entertainment, studies related to computational humor will become increasingly significant in fields such as human-computer interaction, intelligent interactive entertainment, and computer-assisted education. Previous work in computational humor has focused mainly on humor generation, 1,2 and little research has addressed developing systems for automatic humor recognition 3 (see the "Related Work on Computational Humor" sidebar). This is not surprising because, computationally, humor recognition appears to be significantly more subtle and difficult than humor generation. Moreover, the absence of very large collections of humorous texts has hindered the development of systems that use humor in text-based applications. Consequently, few such systems are available. In this article, we explore computational approaches' applicability to the recognition and use of verbally expressed humor. Particularly, we focus on three important research questions related to this problem: Can we automatically gather large collections of humorous texts? Can we automatically recognize humor in text? And can we automatically insert humorous add-ons into existing applications? One-liners versus long jokes Because a deep comprehension of all humor styles is probably too ambitious for existing computational capabilities, we restricted our investigation to oneliners. A one-liner is a short sentence with comic effects and an interesting linguistic structure: simple syntax, deliberate use of rhetoric devices (such as alliteration or rhyme), and frequent use of creative language constructions meant to attract the reader's attention. For instance, "I'm not a vegetarian because I love animals, I'm a vegetarian because I hate plants" is an example of a one-liner. Although longer jokes can have a relatively complex narrative structure, a one-liner must produce the humorous effect in one shot, with few words. This makes one-liners particularly suitable for automatic learning settings because the humor-producing features are guaranteed to be present in the first (and only) sentence. Web-based bootstrapping of humorous one-liners Large amounts of training data can potentially make the learning process more accurate and at the same time provide insights into how increasingly larger data sets can affect classification precision. However, we found that manually constructing a very large one-liner data set was problematic because most Web sites or mailing lists that had such jokes did not list more than 50 to 100 one-liners. To tackle this problem, we implemented a Web-based bootstrapping algorithm that could collect numerous oneliners starting with a short seed list, consisting of a few manually identified one-liners.