Multi-stage entity and relation joint extraction method for water usage structure research field
Previous knowledge extraction models overlooked the intrinsic semantic relationships between entities and,in par-allel,resulted in a significant amount of redundant information when dealing with water usage datasets with complex relationships.To address these issues,this paper proposes a novel entity-relation joint extraction model that integrates semantic information.The model consists of three stages:In the first stage,text information encoded through BERT-wwm is projected into the relation detec-tion space to filter out redundant data in the relation set.In the second stage,a multi-head attention mechanism is employed to fuse relation information into text encoding,obtaining sets of head and tail entities corresponding to each relation.In the third stage,an entity-related matrix incorporating fused contextual semantic information is introduced to accurately extract triplets.Experimental results demonstrate that the designed model achieves a notable entity-relation extraction performance on a water usage structure re-search dataset.
water usage structure researchjoint extractionmulti-stage extractionsemantic informationknowledge graph
NETL
NSTL
万方数据
