Self-Adaptive Named Entity Recognition by Retrieving Unstructured Knowledge

Abstract

Although named entity recognition (NER) assists in extracting domain-specific entities from text (e.g., artists in the music domain), it is expensive to create a large amount of training data or structured knowledge base to perform accurate NER in the target domain. Here, we propose a self-adaptive NER that retrieves external knowledge from unstructured text to learn the usage of entities that have not been learned well. To retrieve useful knowledge for NER, we designed an effective two-stage model that retrieved unstructured knowledge using uncertain entities as queries. Our model predicts the entities in the input and then identifies entities whose predictions are not confident. It then retrieves knowledge by using these uncertain entities as queries and concatenates the retrieved text with the original input to revise the prediction. Experiments on CrossNER datasets demonstrated that our model outperforms strong baselines using 2.35 points in the F₁-metric. We confirmed that knowledge retrieval is important for the NER task and that retrieval based on prediction confidence is particularly useful when the model has long-tail entity knowledge through pre-training.