A Method for Identifying Named Entities in Annotated Word And Sentence Sequences in a Knowledge Base
Network text data has the characteristics of information type diversity,data scale and form variability.It is difficult to accurately mine the text data of the knowledge base,which is difficult to accurately mine the text data,which is easy to lose the problem of text data information,and the identification effect of entity information is not good.In order to solve this problem,a method of named entity recognition in the knowledge base based on graph neural net-work is proposed.This method uses word sentence fusion to represent the text information to avoid the loss of word and sentence information in named entity recognition.Then it removes irrelevant or less relevant word and sentence information through forgetting gate and sigmoid function,retains large relevant information,updates word and sentence information based on tanh function and memory cell unit,uses graph neural network to mine the characteristics and correlation between words and sentences,uses conditional random field and maximum likelihood function to label word and sentence sequences,and determines the content of named entities.Finally,the experiment proves the advancement of the proposed method.Experimental results show that the proposed method significantly improves the recognition ac-curacy of named entities,and has a fast convergence speed and good application effect.
Graph neural networkKnowledge baseWord vectorNamed entitiesLong and short-term memory networks
万方数据
维普
