Telling the Whole Story: A Manually Annotated Chinese Dataset for the Analysis of Humor in Jokes

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pages 6402–6407, Hong Kong, China, November 3–7, 2019. c⃝2019 Association for Computational Linguistics 6402 Telling the Whole Story: A Manually Annotated Chinese Dataset for the Analysis of Humor in Jokes Dongyu Zhang1, Heting Zhang1, Xikai Liu2, Hongfei Lin2, Feng Xia1∗ 1School of Software, Dalian University of Technology, 116620, China 2School of Computer Science and Technology, Dalian University of Technology, 116024, China f.xia@ieee.org hflin@dlut.edu.cn Abstract Humor plays important role in human com- munication, which makes it important problem for natural language processing. Prior work on the analysis of humor focuses on whether tex- t is humorous or not, or the degree of funni- ness, but this is insufficient to explain why it is funny. We therefore create a dataset on humor with 9,123 manually annotated jokes in Chi- nese. We propose a novel annotation scheme t.to give scenarios of how humor arises in tex- Specifically, our annotations of linguistic humor not only contain the degree of funni- ness, like previous work, but they also con- tain key words that trigger humor as well as character relationship, scene, and humor cat- egories. We report reasonable agreement be- tween annotators. We also conduct an analy- sis and exploration of the dataset. To the best of our knowledge, we are the first to approach humor annotation for exploring the underlying mechanism of the use of humor, which may contribute to a significantly deeper analysis of humor. We also contribute with a scarce and valuable dataset, which we will release pub- licly. 1 Introduction Humor plays important role in human communi- cation, which not only serves to exchange ideas or convey messages, but also involves emotion regulation such as provoking laughter, generating amusement, and reducing stress (Wooten, 1996; Morse, 2007). In particular, with the rapid growth of social media applications such as Facebook and Twitter, a significantly increasing number of individuals are using these social media pub- lic platforms to release humorous texts. Hu- mor often arises when two incongruous concept- s are applied and examined through one seman- tic frame (Lefcourt, 2001; Paulos, 2008). The two concepts often involve semantic disconnec- tion in forms such as contradiction and con- trast/comparison. Humor sometimes occurs due to ambiguity (Yang et al., 2015), such as unexpected homophones/homographs. The importance and complexity of humor has thus gained attention in natural language process- ing (NLP), and many computational approaches to it have been proposed (Binsted et al., 2006; Yang et al., 2015; Baziotis et al., 2017; Ortega-Bueno et al., 2018; Liu et al., 2018). Corpora are funda- mental in NLP for sound analysis of humor and for high-quality automatic humor detection. Scholars have been devoted to the study of the humor re- sources in both English and other languages. Mi- halcea and Strapparava (2005) constructed a hu- morous witticism dataset with 16,000 text data for humor identification in English sentences. The dataset comes from one-liners, reuters titles, …

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