BGPNRE:A BERT-based Global Pointer Network for Named Entity-Relation Joint Extraction Method

Abstract

Named entity-relation joint extraction refers to extracting entity-relation triples from unstructured text.It's an important task for information extraction and knowledge graph construction.This paper proposes a new method--BERT-based global pointer network for named entity-relation joint extraction(BGPNRE).Firstly,the potential relation prediction module is used to predict the relations contained in the text,filters out the impossible relations,and limits the predicted relation subset for entity recognition.Then a relation-specific global pointer-net is used to obtain the location of all subject and object entities.Finally,a global pointer network correspondence component is designed to align the subject and object position into named entity-relation triples.This method avoids error propagation frompipeline model,and also solves the the redundancy of relation prediction,entity overlapping,and poor generalization of span-based extraction.Extensive experiments show that our model achieves state-of-the-art performance on NYT and WebNLG public benchmarks with higher performance gain on multi relations and entities overlapping.

Keywords

• named entity-relation joint extraction|bgpnre|global pointer network|bert