  Chinese NER is a difficult undertaking due to the ambiguity of Chinese
characters and the absence of word boundaries. Previous work on Chinese NER
focus on lexicon-based methods to introduce boundary information and reduce
out-of-vocabulary (OOV) cases during prediction. However, it is expensive to
obtain and dynamically maintain high-quality lexicons in specific domains,
which motivates us to utilize more general knowledge resources, e.g., search
engines. In this paper, we propose TURNER: The Uncertainty-based Retrieval
framework for Chinese NER. The idea behind TURNER is to imitate human behavior:
we frequently retrieve auxiliary knowledge as assistance when encountering an
unknown or uncertain entity. To improve the efficiency and effectiveness of
retrieval, we first propose two types of uncertainty sampling methods for
selecting the most ambiguous entity-level uncertain components of the input
text. Then, the Knowledge Fusion Model re-predict the uncertain samples by
combining retrieved knowledge. Experiments on four benchmark datasets
demonstrate TURNER's effectiveness. TURNER outperforms existing lexicon-based
approaches and achieves the new SOTA.
