Semantic Role Labeling of Chinese Adverb Frames Based on BERT Feature Fusion and Dilated Convolution
Chinese frame semantic role labeling plays an important role in Chinese frame semantic analysis.At pres-ent,the task of semantic role labeling in Chinese frame is mainly aimed at verb frame.This paper constructs a Chi-nese adverb framework and dataset,and classifies the word in the framework according to its semantic strength.Then,this paper proposes a semantic role labeling model based on Bert feature fusion and expansion convolution.The model includes four layers,with the bert layer to reperesent the rich semantic information of sentences,the at-tention layer to dynamical weighs the information from each BERT layer,the expansion convolution(IDCNN)layer to extract features,and the CRF layer to predict tags.The model performs well in three adverb frame datasets,a-chieveing 82％or more F1 value.In addition,the model achieves 88.29％F1 value in CFN dataset,which is 4％a-bove the baseline model.
Chinese frame semantic role labelingadverbBERTIDCNNCRF
万方数据
维普
