  In this work, we propose a two-stage method for named entity recognition
(NER), especially for nested NER. We borrowed the idea from the two-stage
Object Detection in computer vision and the way how they construct the loss
function. First, a region proposal network generates region candidates and then
a second-stage model discriminates and classifies the entity and makes the
final prediction. We also designed a special loss function for the second-stage
training that predicts the entityness and entity type at the same time. The
model is built on top of pretrained BERT encoders, and we tried both BERT base
and BERT large models. For experiments, we first applied it to flat NER tasks
such as CoNLL2003 and OntoNotes 5.0 and got comparable results with traditional
NER models using sequence labeling methodology. We then tested the model on the
nested named entity recognition task ACE2005 and Genia, and got F1 score of
85.6$\%$ and 76.8$\%$ respectively. In terms of the second-stage training, we
found that adding extra randomly selected regions plays an important role in
improving the precision. We also did error profiling to better evaluate the
performance of the model in different circumstances for potential improvements
in the future.
