Semantic Role Labeling Based On Deep Attention Neural Network

In recent years,with the rapid development of natural language processing,semantic role tagging,as the most important and basic step in natural language processing,has attracted great attention.Deep neural network is widely used in the task of semantic role tagging,especially in the research of semantic role tagging,which brings a new breakthrough for semantic role tagging.However,with the increase of network layers of deep attention neural network and the increase of horizontal neurons brought by the increase of network layers,the instability of training and the problem of gradient explosion and gradient disappearance appear in the training process.In this case,when using to label data in semantic role labeling system,not only the time-consuming is increased,but also the phenomenon of Karton appears.In order to solve the above problems,this paper optimizes the neural network model of deep attention,accelerates the convergence speed of the model,enhances the network stability and model expression ability.The experimental results show that this method can effectively solve the problem of training difficulty and training instability caused by the gradient information backflow obstruction with the deepening of neural network layers.In this paper,the research and implementation of semantic role labeling based on deep attention neural network are carried out.The main contributions are as follows:(1)aiming at the model training difficulty and instability caused by the increase of the number of layers of neural network,the layer normalization function is introduced to optimize the model of deep attention neural network globally.The experimental results show that the depth attention neural network model optimized by layer normalization function improves the accuracy of semantic role labeling and enhances the stability of the model.(2)Aiming at the problem of gradient explosion and gradient disappearance in the process of model training due to the increase of the number of layers of deep neural network,this paper introduces the deep bidirectional shortterm memory neural network unit based on highway networks optimization to replace the RNN unit of deep attention neural network.The experimental results show that the optimized algorithm model not only improves the accuracy of semantic role labeling,but also alleviates the gradient explosion and gradient disappearance caused by the increase of network layers.(3)In this paper,a semantic role tagging system is designed based on the optimized deep attention neural network model.In addition,the semantic role annotation system improves the efficiency of semantic role annotation,supports semantic role annotation tasks in various scenarios,and manages semantic role annotation tasks..