Modeling Sentiment Association in Discourse for Humor Recognition

Lizhen Liu, Donghai Zhang, Wei Song


Abstract
Humor is one of the most attractive parts in human communication. However, automatically recognizing humor in text is challenging due to the complex characteristics of humor. This paper proposes to model sentiment association between discourse units to indicate how the punchline breaks the expectation of the setup. We found that discourse relation, sentiment conflict and sentiment transition are effective indicators for humor recognition. On the perspective of using sentiment related features, sentiment association in discourse is more useful than counting the number of emotional words.
Anthology ID:
P18-2093
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Iryna Gurevych, Yusuke Miyao
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
586–591
Language:
URL:
https://aclanthology.org/P18-2093
DOI:
10.18653/v1/P18-2093
Bibkey:
Cite (ACL):
Lizhen Liu, Donghai Zhang, and Wei Song. 2018. Modeling Sentiment Association in Discourse for Humor Recognition. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 586–591, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Modeling Sentiment Association in Discourse for Humor Recognition (Liu et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-2093.pdf
Poster:
 P18-2093.Poster.pdf