  In many scenarios, named entity recognition (NER) models severely suffer from
unlabeled entity problem, where the entities of a sentence may not be fully
annotated. Through empirical studies performed on synthetic datasets, we find
two causes of performance degradation. One is the reduction of annotated
entities and the other is treating unlabeled entities as negative instances.
The first cause has less impact than the second one and can be mitigated by
adopting pretraining language models. The second cause seriously misguides a
model in training and greatly affects its performances. Based on the above
observations, we propose a general approach, which can almost eliminate the
misguidance brought by unlabeled entities. The key idea is to use negative
sampling that, to a large extent, avoids training NER models with unlabeled
entities. Experiments on synthetic datasets and real-world datasets show that
our model is robust to unlabeled entity problem and surpasses prior baselines.
On well-annotated datasets, our model is competitive with the state-of-the-art
method.
