Abstract: 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), entity 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 entity 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 experimental 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.

Key words: pre-judgment of span, entity relation extraction, BERT pretraining model, multi-round entity classification, multi-round relation classification