Before revision:  The core idea is using negative sampling to keep the probability of training with unlabeled entities at a very low level . Experiments on synthetic datasets and real-world datasets show that our model is robust to unlabeled entity problem and surpasses prior baselines.
After revision:  The key idea is to use negative sampling that, to a large extent, avoids training NER models with unlabeled entities at a very low level . Experiments on synthetic datasets and real-world datasets show that our model is robust to unlabeled entity problem and surpasses prior baselines.