Self-Adaptive Named Entity Recognition by Retrieving Unstructured Knowledge

Although named entity recognition (NER) helps us to extract various 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, where the model retrieves the external knowledge from unstructured text to learn the usage of entities that has 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 first predicts the entities in the input and then finds the entities of which the prediction is not confident. Then, our model 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 the strong NERBERT baseline by 2.45 points on average.