  When performing named entity recognition (NER), entity length is variable and
dependent on a specific domain or dataset. Pre-trained language models (PLMs)
are used to solve NER tasks and tend to be biased toward dataset patterns such
as length statistics, surface form, and skewed class distribution. These biases
hinder the generalization ability of PLMs, which is necessary to address many
unseen mentions in real-world situations. We propose a novel debiasing method
RegLER to improve predictions for entities of varying lengths. To close the gap
between evaluation and real-world situations, we evaluated PLMs on partitioned
benchmark datasets containing unseen mention sets. Here, RegLER shows
significant improvement over long-named entities that can predict through
debiasing on conjunction or special characters within entities. Furthermore,
there is a severe class imbalance in most NER datasets, causing easy-negative
examples to dominate during training, such as "The". Our approach alleviates
skewed class distribution by reducing the influence of easy-negative examples.
Extensive experiments on the biomedical and general domains demonstrated the
generalization capabilities of our method. To facilitate reproducibility and
future work, we release our code."https://github.com/minstar/RegLER"

Computation and Language;Artificial Intelligence