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.

本研究探索了类增量 NER 中的隐藏实体问题，并提出了一种基于重演的表示学习方法，既可以适当地学习旧实体类的“O”类，也可以学习新实体类的“O”类，通过实验验证了提出方法在多类增量NER上的有效性。

学习“O”有助于更多的学习：处理隐含实体问题的类增量NER算法