Named Entity Recognition is the task to locate and classify the entities in
the text. However, Unlabeled Entity Problem in NER datasets seriously hinders
the improvement of NER performance. This paper proposes SCL-RAI to cope with
this problem. Firstly, we decrease the distance of span representations with
the same label while increasing it for different ones via span-based
contrastive learning, which relieves the ambiguity among entities and improves
the robustness of the model over unlabeled entities. Then we propose retrieval
augmented inference to mitigate the decision boundary shifting problem. Our
method significantly outperforms the previous SOTA method by 4.21% and 8.64%
F1-score on two real-world datasets.

本文提出 SCL-RAI 方法解决 NER 数据集中未标注实体问题，并通过基于 span 的对比学习缓解实体歧义和提高模型对未标注实体的鲁棒性，同时提出基于检索的推断以减轻决策边界偏移问题，该方法在两个真实世界数据集上比之前的 SOTA 水平提高了 4.21% 和 8.64% 的 F1 得分。