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。

SCL-RAI：基于跨度的对比学习，结合检索增强推理用于 NER 中未标注实体问题

SCL-RAI: Span-based Contrastive Learning with Retrieval Augmented Inference for Unlabeled Entity Problem in NER

In many scenarios, named entity recognition (NER) models severely suffer from
unlabeled entity problem, where the entities of a sentence may not be fully
annotated. Through empirical studies performed on synthetic datasets, we find
two causes of performance degradation. One is the reduction of annotated
entities and the other is treating unlabeled entities as negative instances.
The first cause has less impact than the second one and can be mitigated by
adopting pretraining language models. The second cause seriously misguides a
model in training and greatly affects its performances. Based on the above
observations, we propose a general approach, which can almost eliminate the
misguidance brought by unlabeled entities. The key idea is to use negative
sampling that, to a large extent, avoids training NER models with unlabeled
entities. Experiments on synthetic datasets and real-world datasets show that
our model is robust to unlabeled entity problem and surpasses prior baselines.
On well-annotated datasets, our model is competitive with the state-of-the-art
method.

通过使用负采样，避免 NER 模型的训练受到未标注实体的干扰，提出了一种能够有效应对未标注实体问题的方法。在实验中，该方法表现出很强的鲁棒性，甚至能够超越现有的基线模型。