A Noise-Robust Loss for Unlabeled Entity Problem in Named Entity Recognition

@article{Kang2022ANL,
  title={A Noise-Robust Loss for Unlabeled Entity Problem in Named Entity Recognition},
  author={Wentao Kang and Guijun Zhang and Xiao Fu},
  journal={ArXiv},
  year={2022},
  volume={abs/2208.02934}
}
Named Entity Recognition (NER) is an important task in natural language processing. However, traditional supervised NER requires large-scale annotated datasets. Distantly supervision is proposed to alleviate the massive demand for datasets, but datasets constructed in this way are extremely noisy and have a se-rious unlabeled entity problem. The cross entropy (CE) loss function is highly sensitive to unlabeled data, leading to severe performance degradation. As an alternative, we propose a new… 

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