  More recently, Named Entity Recognition hasachieved great advances aided by
pre-trainingapproaches such as BERT. However, currentpre-training techniques
focus on building lan-guage modeling objectives to learn a gen-eral
representation, ignoring the named entity-related knowledge. To this end, we
proposea NER-specific pre-training framework to in-ject coarse-to-fine
automatically mined entityknowledge into pre-trained models. Specifi-cally, we
first warm-up the model via an en-tity span identification task by training it
withWikipedia anchors, which can be deemed asgeneral-typed entities. Then we
leverage thegazetteer-based distant supervision strategy totrain the model
extract coarse-grained typedentities. Finally, we devise a
self-supervisedauxiliary task to mine the fine-grained namedentity knowledge
via clustering.Empiricalstudies on three public NER datasets demon-strate that
our framework achieves significantimprovements against several pre-trained
base-lines, establishing the new state-of-the-art per-formance on three
benchmarks. Besides, weshow that our framework gains promising re-sults without
using human-labeled trainingdata, demonstrating its effectiveness in label-few
and low-resource scenarios
