Negative sampling is highly effective in handling missing annotations for
named entity recognition (NER). One of our contributions is an analysis on how
it makes sense through introducing two insightful concepts: missampling and
uncertainty. Empirical studies show low missampling rate and high uncertainty
are both essential for achieving promising performances with negative sampling.
Based on the sparsity of named entities, we also theoretically derive a lower
bound for the probability of zero missampling rate, which is only relevant to
sentence length. The other contribution is an adaptive and weighted sampling
distribution that further improves negative sampling via our former analysis.
Experiments on synthetic datasets and well-annotated datasets (e.g.,
CoNLL-2003) show that our proposed approach benefits negative sampling in terms
of F1 score and loss convergence. Besides, models with improved negative
sampling have achieved new state-of-the-art results on real-world datasets
(e.g., EC).

本文介绍了关于如何使用负采样处理命名实体识别任务的缺失注释的分析和改进方法，通过引入 missampling 和 uncertainty 两个概念，实验证明负采样的低错误率和高不确定性是实现良好表现的关键，并提出了一种自适应和加权采样方法提高了负采样性能，证明在合成数据集以及 CoNLL-2003 数据集中，改进后的负采样方法能够提高 F1 得分且 loss 收敛速度更快，更进一步在实际数据集比如 EC 上取得了最新的最佳效果。