Named entity recognition is a traditional task in natural language
processing. In particular, nested entity recognition receives extensive
attention for the widespread existence of the nesting scenario. The latest
research migrates the well-established paradigm of set prediction in object
detection to cope with entity nesting. However, the manual creation of query
vectors, which fail to adapt to the rich semantic information in the context,
limits these approaches. An end-to-end entity detection approach with proposer
and regressor is presented in this paper to tackle the issues. First, the
proposer utilizes the feature pyramid network to generate high-quality entity
proposals. Then, the regressor refines the proposals for generating the final
prediction. The model adopts encoder-only architecture and thus obtains the
advantages of the richness of query semantics, high precision of entity
localization, and easiness of model training. Moreover, we introduce the novel
spatially modulated attention and progressive refinement for further
improvement. Extensive experiments demonstrate that our model achieves advanced
performance in flat and nested NER, achieving a new state-of-the-art F1 score
of 80.74 on the GENIA dataset and 72.38 on the WeiboNER dataset.

本文提出了一个基于 proposer 和 regressor 的端到端实体检测方法，采用编码器架构，使用空间调制注意力和渐进细化方法来进一步提高性能，模型在平面和嵌套类型的命名实体识别任务中均获得了先进性能，在 GENIA 数据集上达到了 80.74 的 F1 分数，在 WeiboNER 数据集上达到了 72.38。