  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).
