Recent studies in deep learning have shown significant progress in named
entity recognition (NER). Most existing works assume clean data annotation, yet
a fundamental challenge in real-world scenarios is the large amount of noise
from a variety of sources (e.g., pseudo, weak, or distant annotations). This
work studies NER under a noisy labeled setting with calibrated confidence
estimation. Based on empirical observations of different training dynamics of
noisy and clean labels, we propose strategies for estimating confidence scores
based on local and global independence assumptions. We partially marginalize
out labels of low confidence with a CRF model. We further propose a calibration
method for confidence scores based on the structure of entity labels. We
integrate our approach into a self-training framework for boosting performance.
Experiments in general noisy settings with four languages and distantly labeled
settings demonstrate the effectiveness of our method. Our code can be found at
this https URL

该研究采用校准置信度估计的方式来研究深度学习在实际场景下的命名实体识别问题，提出了一种基于本地和全局独立性假设的置信得分估计策略，并结合 CRF 模型来隔离低置信度标签，同时提出了一种基于实体标签结构的置信度校准方法，最后将其集成到自我训练框架中进行性能提升，在四种语言和遥远标注场景下的实验证明了该方法的有效性。