As the categories of named entities rapidly increase in real-world
applications, class-incremental learning for NER is in demand, which
continually learns new entity classes while maintaining the old knowledge. Due
to privacy concerns and storage constraints, the model is required to update
without any annotations of the old entity classes. However, in each step on
streaming data, the "O" class in each step might contain unlabeled entities
from the old classes, or potential entities from the incoming classes. In this
work, we first carry out an empirical study to investigate the concealed entity
problem in class-incremental NER. We find that training with "O" leads to
severe confusion of "O" and concealed entity classes, and harms the
separability of potential classes. Based on this discovery, we design a
rehearsal-based representation learning approach for appropriately learning the
"O" class for both old and potential entity classes. Additionally, we provide a
more realistic and challenging benchmark for class-incremental NER which
introduces multiple categories in each step. Experimental results verify our
findings and show the effectiveness of the proposed method on the new
benchmark