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

Dongyu Zhang, Heting Zhang, Xikai Liu, Hongfei Lin, Feng Xia

Abstract
Humor plays important role in human communication, which makes it important problem for natural language processing. Prior work on the analysis of humor focuses on whether text is humorous or not, or the degree of funniness, but this is insufficient to explain why it is funny. We therefore create a dataset on humor with 9,123 manually annotated jokes in Chinese. We propose a novel annotation scheme to give scenarios of how humor arises in text. Specifically, our annotations of linguistic humor not only contain the degree of funniness, like previous work, but they also contain key words that trigger humor as well as character relationship, scene, and humor categories. We report reasonable agreement between annota-tors. We also conduct an analysis 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 publicly.
Anthology ID:
D19-1673
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
6402–6407
Language:
URL:
https://aclanthology.org/D19-1673
DOI:
10.18653/v1/D19-1673
Bibkey:
Cite (ACL):
Dongyu Zhang, Heting Zhang, Xikai Liu, Hongfei Lin, and Feng Xia. 2019. Telling the Whole Story: A Manually Annotated Chinese Dataset for the Analysis of Humor in Jokes. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6402–6407, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Telling the Whole Story: A Manually Annotated Chinese Dataset for the Analysis of Humor in Jokes (Zhang et al., EMNLP-IJCNLP 2019)
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https://preview.aclanthology.org/improve-issue-templates/D19-1673.pdf
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