Joint Extraction Model for Entity Relationships Based on Span and Boundary Detection
LIAO Tao
XU Jintao
Abstract：In view of the fact that when most span models divided text into span sequences,a large number of non-entity spans were generated,which led to problems such as data imbalance and high computational complexity.This paper proposed a joint extraction model of entity relationships based on span and boundary detection(SBDM).The model first used the bidirectional encoder representations from Transformer(BERT)model to convert text into word vectors,and integrated syntactic dependency information obtained through graph convolution to form a feature representation of the text.Next,it used local information and sentence context information to detect and mark entity boundaries,thereby reducing non-entity spans.Then,the span sequence formed by entity boundary markers was used for entity recognition.Finally,local context information was fused into a span pair and the sigmoid function was used for relationship classification.The experiment showed that SBDM achieved good results in relation classification SF1 values of 52.86%and 74.47%on the multi-task identification of entities,relations,and coreference for scientific knowledge graph construction(SciERC)dataset and the 2004 conference on natural language learning(CoNLL04)dataset,respectively.The use of SBDM in relation classification tasks promotes the research of spanning classification methods in relation.
Keywords：entity relationshipjoint extractionsyntactic dependencyspanentity boundarygraph convolutionrelationship classification
Publication Date：2024-06-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 178-184 )