  Successful Machine Learning based Named Entity Recognition models could fail
on texts from some special domains, for instance, Chinese addresses and
e-commerce titles, where requires adequate background knowledge. Such texts are
also difficult for human annotators. In fact, we can obtain some potentially
helpful information from correlated texts, which have some common entities, to
help the text understanding. Then, one can easily reason out the correct answer
by referencing correlated samples. In this paper, we suggest enhancing NER
models with correlated samples. We draw correlated samples by the sparse BM25
retriever from large-scale in-domain unlabeled data. To explicitly simulate the
human reasoning process, we perform a training-free entity type calibrating by
majority voting. To capture correlation features in the training stage, we
suggest to model correlated samples by the transformer-based multi-instance
cross-encoder. Empirical results on datasets of the above two domains show the
efficacy of our methods.
