Humor Recognition Based on Latent Semantic Features
FAN Xiaochao1,2, YANG Liang2, LIN Hongfei2, DIAO Yufeng2,3, SHEN Chen2, CHU Yonghe2, ZHANG Tongxuan2
1.School of Computer Science and Technology, Xinjiang Normal University, Urumqi, Xinjiang 830054, China; 2.School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning 116024, China; 3.School of Computer Science and Technology, Inner Mongolia University for Nationalities, Tongliao, Inner Mongolia 028043, China
Abstract：Humor recognition is a challenge in the field of natural language processing. According to humor theories and cognitive linguistics, five types of distinctive aspects of humor are systematically analyzed, and a variety of humor features are derived. The experiment results show that the proposed features can better represent the latent semantic information of humor. Furthermore, the deep learning can benefit from these features for humor recognition.
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2021, Vol. 35 
