2025
Token-Level Metrics for Detecting Incorrect Gold Annotations in Named Entity Recognition
EMNLP 2025
Annotated datasets for supervised learning tasks often contain incorrect gold annotations, i.e. label noise. To address this issue, many noisy label learning approaches incorporate metrics to filter out unreliable samples, for example using heuristics such as high loss or low confidence. However, wh