Abstract: Extracting entities and relations from unstructured text is the fundamental task of automatically constructing know-ledge bases.Existing works mainly adopt joint learning to solve the problems of nested entities,overlapping relations,redundant computation,or exposure bias,but a single model only performs well on some issues,and no model can solve the above problems simultaneously.Therefore,a single-stage joint entity and relation extraction method based on an enhanced sequence annotation strategy called ATMREL is proposed.First,an enhanced sequence annotation strategy is designed to tag each word in the text with multiple labels,and the labels contain information about the position of each word in the entity,the relation type and the entity location.Second,the labels prediction of each word is transformed into a multi-label classification task,while the joint entity and relation extraction is transformed into a sequence annotation task.Finally,to enhance the dependencies between entity pairs,an entity correlation matrix is introduced for pruning the extraction results to improve the model extraction effect.Experimental results show that ATMREL model reduces the parameter volume by 3.1×10⁶~5.4×10⁶,improves the average inference speed by 2~4.2 times,and improves the F1 value by 0.5%~2.1% compared with the CasRel and TPLinker models on the NYT and WebNLG datasets.

Key words: Joint entity and relation extraction, Sequence annotation, Combined labels, Correlation matrix