These label mistakes bring up two challenges to NER: (1) mistakes in the test set can interfere the evaluation results and even lead to an inaccurate assessment of model performance; and (2) mistakes in the training set can hurt NER model training.
in this paper, we conduct empirical studies to understand these mistakes, correct the mistakes in the test set to form a cleaner benchmark, and develop a novel framework to handle the mistakes in the training set.