Humor Recognition Based on Linguistic Features and Hierarchical Attention Mechanism

Abstract

This paper proposes a hierarchical attention mechanism neural network model based on the features of pronunciation,font and semantics(PFSHAN) for humor recognition,which extracts the features of English humor linguistics.During the feature extraction stage,the humor texts are presented phoneme,character and semantic information that carries ambiguity level information,and then the features of pronunciation,font and semantics of the PFSHAN model are extracted by using the Convolutional Neural Network(CNN),Bi-directional Gated Recurrent Unit(Bi-GRU),and the attention mechanism.During the feature fusion stage,as words contribute differently to the linguistic features of humors,and the linguistic features of humors are also correlated differently to sentences,the hierarchical attention mechanism is used to adjust the influence of different linguistic features on performance of the PFSHAN model.Experimental results on datasets of Puns and Onliner show that the F1 scores of the PFSHAN model are 91.03% and 91.11% respectively,significantly improving the humor recognition performance.