Abstract
Humor is an essential component in personal communication. How to create computational models to discover the structures behind humor, recognize humor and even extract humor anchors remains a challenge. In this work, we first identify several semantic structures behind humor and design sets of features for each structure, and next employ a computational approach to recognize humor. Furthermore, we develop a simple and effective method to extract anchors that enable humor in a sentence. Experiments conducted on two datasets demonstrate that our humor recognizer is effective in automatically distinguishing between humorous and non-humorous texts and our extracted humor anchors correlate quite well with human annotations.
Cite
CITATION STYLE
Yang, D., Lavie, A., Dyer, C., & Hovy, E. (2015). Humor recognition and humor anchor extraction. In Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing (pp. 2367–2376). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/d15-1284
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