In many situations (e.g., distant supervision), unlabeled entity problem seriously degrades the performances of named entity recognition (NER) models. Recently, this issue has been well addressed by a notable approach based on negative sampling. In this work, we perform two studies along this direction. Firstly, we analyze why negative sampling succeeds both theoretically and empirically. Based on the observation that named entities are highly sparse in datasets, we show a theoretical guarantee that, for a long sentence, the probability of containing no unlabeled entities in sampled negatives is high. Missampling tests on synthetic datasets have verified our guarantee in practice. Secondly, to mine hard negatives and further reduce missampling rates, we propose a weighted and adaptive sampling distribution for negative sampling. Experiments on synthetic datasets and well-annotated datasets show that our method significantly improves negative sampling in robustness and effectiveness. We also have achieved new state-of-the-art results on real-world datasets.
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