  Distantly-Supervised Named Entity Recognition effectively alleviates the
burden of time-consuming and expensive annotation in the supervised setting.
But the context-free matching process and the limited coverage of knowledge
bases introduce inaccurate and incomplete annotation noise respectively.
Previous studies either considered only incomplete annotation noise or
indiscriminately handle two types of noise with the same strategy. In this
paper, we argue that the different causes of two types of noise bring up the
requirement of different strategies in model architecture. Therefore, we
propose the SANTA to handle these two types of noise separately with (1)
Memory-smoothed Focal Loss and Entity-aware KNN to relieve the entity ambiguity
problem caused by inaccurate annotation, and (2) Boundary Mixup to alleviate
decision boundary shifting problem caused by incomplete annotation and a
noise-tolerant loss to improve the robustness. Benefiting from our separate
tailored strategies, we confirm in the experiment that the two types of noise
are well mitigated. SANTA also achieves a new state-of-the-art on five public
datasets.
