Mislabeled examples are a common issue in real-world data, particularly for tasks like token classification where many labels must be chosen on a fine-grained basis. Here we consider the task of finding sentences that contain label errors in token classification datasets. We study 11 different straightforward methods that score tokens/sentences based on the predicted class probabilities output by a (any) token classification model (trained via any procedure). In precision-recall evaluations based on real-world label errors in entity recognition data from CoNLL-2003, we identify a simple and effective method that consistently detects those sentences containing label errors when applied with different token classification models.
@article{arxiv.2210.03920,
title = {Detecting Label Errors in Token Classification Data},
author = {Wei-Chen Wang and Jonas Mueller},
journal= {arXiv preprint arXiv:2210.03920},
year = {2022}
}