Joint Extraction of Entities and Relations Based on Word-Pair Distance Embedding and Axial Attention Mechanism
The joint extraction of entities and relations provides key technical support for the construction of knowledge graphs,and the problem of overlapping relations has always been the focus of joint extraction model research.Many of the existing me-thods use multi-step modeling methods.Although they have achieved good results in solving the problem of overlapping rela-tions,they have produced the problem of exposure bias.In order to solve the problem of overlapping relations and exposure bias at the same time,a joint entities and relations extraction method(DE-AA)based on word-pair distance embedding and axial atten-tion mechanism is proposed.Firstly,the table features of the representative word-pair relation are constructed,and the word-pair distance feature information is added to optimize its representation.Secondly,the axial attention model based on row attention and column attention is applied to enhance the table features,which can reduce the computational complexity while fusing the global features.Finally,the table features are mapped to each relation space to generate the relation-specific word-pair relation table,and the table filling method is used to assign labels to each item in the table,and the triples are extracted by triple classification.The proposed model is evaluated on the public datasets NYT and WebNLG.Experimental results show that the proposed model achieves better performance than other baseline models,and has significant advantages in dealing with overlapping relations or multiple relations.
Joint extraction of entities and relationsAxial attention mechanismWord-Pair distance embeddingTable filling method
NETL
NSTL
万方数据
