In existing research on nested named entity recognition,this task is treated as span classification tasks via finetuned pretrained models.This paper proposes a multi-head model based on knowledge embedding(MKE for short)method to further improve this task.This method introduces domain-specific knowledge in the form of entity matrices,allowing the background knowledge to be embedded without any loss.It also transforms the named entity recognition into a multi-head selection process,followed by scoring the candidate spans using the attention score model.The experimental results show that the proposed method achieves the state-of-the-art performance on seven nested and flat named entity recognition datasets.
nested named entity recognitionknowledge embeddingmulti-head selectionattentionentity multi-clas-sification
NETL
NSTL
万方数据
