Homographic puns have a long history in human writing, being a common source of humor in jokes and other comedic works. It remains a difficult challenge to construct computational models to discover the latent semantic structures behind homographic puns so as to recognize puns. In this work, we design several latent semantic structures of homographic puns based on relevant theory and design sets of effective features of each structure, and then we apply an effective computational approach to identify homographic puns. Results on the SemEval2017 Task7 and Pun of the Day datasets indicate that our proposed latent semantic structures and features have sufficient effectiveness to distinguish between homographic pun and non-homographic pun texts. We believe that our novel findings will facilitate and stimulate the booming field of computational pun research in the future.
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