  Most named entity recognition (NER) systems focus on improving model
performance, ignoring the need to quantify model uncertainty, which is critical
to the reliability of NER systems in open environments. Evidential deep
learning (EDL) has recently been proposed as a promising solution to explicitly
model predictive uncertainty for classification tasks. However, directly
applying EDL to NER applications faces two challenges, i.e., the problems of
sparse entities and OOV/OOD entities in NER tasks. To address these challenges,
we propose a trustworthy NER framework named E-NER by introducing two
uncertainty-guided loss terms to the conventional EDL, along with a series of
uncertainty-guided training strategies. Experiments show that E-NER can be
applied to multiple NER paradigms to obtain accurate uncertainty estimation.
Furthermore, compared to state-of-the-art baselines, the proposed method
achieves a better OOV/OOD detection performance and better generalization
ability on OOV entities.
