Research And Application Of Semantic Role Labeling Based On Deep Attention Neural Network

For many years,semantic analysis has been a research focus and difficulty in the field of natural language processing,and it is also a major technical bottleneck in the application of information retrieval,machine translation,and human-machine dialogue systems.As one of the basic tasks in semantic analysis,semantic role labeling plays an important role in promoting the development and progress of semantic analysis technology.At present,the semantic role labeling based on deep attention neural network has achieved good results,but the model also has some problems,such as the syntactic dependency information is not fully extracted by the model,and the representation ability of the network is insufficient.To this end,this paper models the semantic role labeling task based on the deep attention neural network model,focusing on the representation and merging of syntactic dependency information in modeling,as well as the method to enhance the representation ability of the network,and constructs a semantic role labeling system.The main work of the paper includes the following aspects:(1)The syntactic dependency information in the deep attention neural network model is not fully extracted,and the existing syntactic knowledge is insufficient.The performance of three syntactic dependency parsers is analyzed and compared,and the encoding representation of syntactic dependency information and the way of incorporating syntactic dependency information in the self-attention sub-layer are given.The results show that adding syntactic dependency information to the self-attention sub-layer of the model can help to improve the accuracy of annotation.(2)Aiming at the problem of insufficient representation ability of the nonlinear transformation sub-layer RNN unit in the deep attention neural network model,firstly use the deep bidirectional long short-term memory neural network to replace the RNN unit of the original model,and then use the elevator unit gate to balance the LSTM unit Gradient transfer in the vertical direction to alleviate the problem of gradient disappearance.The results show that the method can significantly enhance the flexibility of the neural network and improve the representation ability of the network.(3)Based on the improved deep attention neural network semantic role labeling model,a semantic role annotation system is constructed,including requirements analysis,functional summary design,technical selection,and detailed design and implementation,which improves the performance of the semantic role labeling system.