Aiming at entity recognition and relation extraction tasks in natural language processing,a model named Smrc is proposed,which makes predictions at the token sequence(span)level.The model uses BERT pre-training model as an encoder and include three modules:entity pre-judgment(Pej),en-tity multi-round classification(Emr)and relation multi-round classification(Rmr).The Smrc model performs entity recognition through the preliminary judgment of the Pej module and the multi-round en-tity classification of the Emr module,and then uses the Rmr module's multi-round relation classification to determine the relationships between entities,thus completing the relation extraction task.On the ex-perimental datasets of CoNLL04,SciERC,and ADE,the F1 values of entity recognition reach 89.67%,70.62%,and 89.56%,respectively,and the F1 values of relation extraction reach 73.11%,51.03%,and 79.89%,respectively.Compared with the previous best model Spert on the three datasets,the Smrc model achieves improvements of 0.73%,0.29%,and 0.61%in entity recognition and 1.64%,0.19%,and 1.05%in relation extraction through entity pre-judgment and multi-round classification of entities and relations,which demonstrates the effectiveness and advantages of the model.
维普
万方数据