Frame Identification(FI),which aims to find the proper frame to activate for a target words in a given sentence,is an important prerequisite for labeling frame semantic roles.Generally,FI is regarded as a classifying task,applying the sequence modeling to learn the contextual representation of target words.To further capture the structural information of target words themselves,this paper proposes a model which fuses the contextual and structural information of target words.Specifically,BERT and GCN are utilized to model the contextual information of target words in different parts of speech and the structural information of target words in PropBank roles or de-pendence syntax,respectively.Also,this paper analyzes the structural differences of the dependency information of target words with different parts of speech,and employs an ensemble learning approach to consider the structural differences.Experiments on FN1.7 and CFN datasets show that our model outperforms the SOTA.
NETL
NSTL
万方数据
