  Although named entity recognition (NER) helps us to extract domain-specific
entities from text (e.g., artists in the music domain), it is costly to create
a large amount of training data or a structured knowledge base to perform
accurate NER in the target domain. Here, we propose self-adaptive NER, which
retrieves external knowledge from unstructured text to learn the usages of
entities that have not been learned well. To retrieve useful knowledge for NER,
we design an effective two-stage model that retrieves unstructured knowledge
using uncertain entities as queries. Our model predicts the entities in the
input and then finds those of which the prediction is not confident. Then, it
retrieves knowledge by using these uncertain entities as queries and
concatenates the retrieved text to the original input to revise the prediction.
Experiments on CrossNER datasets demonstrated that our model outperforms strong
baselines by 2.35 points in F1 metric.
