  Everyone makes mistakes. So do human annotators when curating labels for
named entity recognition (NER). Such label mistakes might hurt model training
and interfere model comparison. In this study, we dive deep into one of the
widely-adopted NER benchmark datasets, CoNLL03 NER. We are able to identify
label mistakes in about 5.38% test sentences, which is a significant ratio
considering that the state-of-the-art test F1 score is already around 93%.
Therefore, we manually correct these label mistakes and form a cleaner test
set. Our re-evaluation of popular models on this corrected test set leads to
more accurate assessments, compared to those on the original test set. More
importantly, we propose a simple yet effective framework, CrossWeigh, to handle
label mistakes during NER model training. Specifically, it partitions the
training data into several folds and train independent NER models to identify
potential mistakes in each fold. Then it adjusts the weights of training data
accordingly to train the final NER model. Extensive experiments demonstrate
significant improvements of plugging various NER models into our proposed
framework on three datasets. All implementations and corrected test set are
available at our Github repo: https://github.com/ZihanWangKi/CrossWeigh.
