  Recent advances in Named Entity Recognition (NER) show that document-level
contexts can significantly improve model performance. In many application
scenarios, however, such contexts are not available. In this paper, we propose
to find external contexts of a sentence by retrieving and selecting a set of
semantically relevant texts through a search engine, with the original sentence
as the query. We find empirically that the contextual representations computed
on the retrieval-based input view, constructed through the concatenation of a
sentence and its external contexts, can achieve significantly improved
performance compared to the original input view based only on the sentence.
Furthermore, we can improve the model performance of both input views by
Cooperative Learning, a training method that encourages the two input views to
produce similar contextual representations or output label distributions.
Experiments show that our approach can achieve new state-of-the-art performance
on 8 NER data sets across 5 domains.
