Nested Entity Recognition Fusing Span Relative Position and Region Information.

At present, span-based entity recognition methods are mainly used to accurately identify the span (entity) boundary for entity recognition, in which the relative position information of the span boundary and the information of words in the span region are routinely ignored. This information can be used to improve entity recognition performance. Therefore, a nested entity recognition model, which integrates the relative position information of the span and the region information within the span, is proposed. The span representation is first obtained with a triaffine attention. Then, the relative position of the span boundary and the word information in the span region, as well as the previous span representation, are fused to obtain a new label-level span representation with another triaffine attention. Finally, the span (entity) recognition task is carried out by a cooperative biaffine mechanism. Experiments were conducted on some public datasets, including ACE2004, ACE2005 and GENIA. The results show that the F1-scores achieved using the proposed method were 87.66%, 86.86% and 80.90% on ACE2004, ACE2005 and GENIA, respectively. These experiments show that the method achieved state-of-the-art (SOTA) results. Moreover, the proposed model has fewer parameters and needs fewer resources with a lower time complexity than the existing triaffine mechanism model. [ABSTRACT FROM AUTHOR]