  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.
