Joke retrieval: recognizing the same joke told differently

In a corpus of jokes, a human might judge two documents to be the "same joke" even if characters, locations, and other details are varied. A given joke could be retold with an entirely different vocabulary while still maintaining its identity. Since most retrieval systems consider documents to be related only when their word content is similar, we propose joke retrieval as a domain where standard language models may fail. Other meaning-centric domains include logic puzzles, proverbs and recipes; in such domains, new techniques may be required to enable us to search effectively. For jokes, a necessary component of any retrieval system will be the ability to identify the "same joke," so we examine this task in both ranking and classification settings. We exploit the structure of jokes to develop two domain-specific alternatives to the "bag of words" document model. In one, only the punch lines, or final sentences, are compared; in the second, certain categories of words (e.g., professions and countries) are tagged and treated as interchangeable. Each technique works well for certain jokes. By combining the methods using machine learning, we create a hybrid that achieves higher performance than any individual approach.