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Joke Retrieval: Recognizing the Same Joke Told Differently
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@MISC{Friedl_jokeretrieval:,
author = {Lisa Friedl and James Allan},
title = {Joke Retrieval: Recognizing the Same Joke Told Differently},
year = {}
}
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Abstract
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. In particular, we consider the task of identifying the "same joke " to be a necessary component of any joke retrieval system, and we examine it 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 marked up 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.
Keyphrases
joke retrieval joke told differently standard language model certain category word content retrieval system punch line classification setting word document model individual approach different vocabulary machine learning domain-specific alternative joke retrieval system final sentence necessary component certain joke
