Abstract: Entity and relation extraction task is the core task of information extraction.It plays an irreplaceable role in effectively extracting key information from explosive growth data,and is also the basic task of building a large-scale knowledge graph.Therefore,the research on entity relationship extraction task is of great significance for various natural language processing(NLP) tasks.Although the existing entity and relation extraction based on deep learning method has a very mature theory and good performance,there are still some problems,such as error accumulation,entity redundancy,lack of interaction,entity and relation overlap.Semantic information and syntactic information play an important role in NLP tasks.In order to make full use of them to solve the above problems,a fusion of semantic and syntactic graph convolutional networks binary tagging framework for relation triple extraction(FSSRel) is proposed.The model is divided into three stages.In the first stage,the start and end positions of the triple body are predicted.In the second stage,semantic features and syntactic features are extracted by semantic graph neural network and syntactic graph neural network respectively,and fused into the coding vector.In the third stage,the object position of each relation of the statement is predicted and marked to complete the extraction of the final triple.Experimental results show that the F1 value of the model increases by 2.5% and 1.6% respectively compared with the baseline model on the NYT dataset and the WebNLG dataset,and it also performs well on complex data with overlapping triples and multiple triples.

Key words: Joint entity and relation extraction, Semantic information, Syntactic dependency analysis, Graph convolution neural network