Sarcasm recognition based on multi-dimensional semantic features and hierarchical attention mechanism
Sarcasm is a complex language expression that plays an important role in everyday communication.With the rapid development of artificial intelligence and social networks,making computers to automatically recognize sar-casm has become one of the hot research topics in the field of natural language processing.Existing research on sar-casm recognition often expresses samantic features from a single dimension,ignoring the subtle differences and im-portance of samantic features.This paper treats sarcasm recognition as a kind of natural language classification task,in the feature extraction stage,the sarcasm text is represented by multi-dimensional semantic features accord-ing to its inconsistency features,affective features,dependency structure features and style features.In the feature fusion stage,the hierarchical attention mechanism is used to adjust the impact of different samantic linguistic fea-tures on the overall performance of the model in view of the different contribution and correlation degree of different dimension features to the overall feature.The experimental results show that the proposed model can extract the la-tent semantic features of satirical text from multiple dimensions,bring a significant improvement on public datasets IAC,Tweets and Reddit.
sarcasm recognitionnatural language processingmulti-dimensional semantichierarchical atten-tion mechanism
万方数据
维普
