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We present a weakly-supervised data augmentation approach to improve NamedEntity Recognition (NER) in a challenging domain: extracting biomedicalentities (e.g., proteins) from the scientific literature. First, we train aneural NER (NNER) model over a small seed of fully-labeled examples. Second, weuse a reference set of entity names (e.g., proteins in UniProt) to identifyentity mentions with high precision, but low recall, on an unlabeled corpus.Third, we use the NNER model to assign weak labels to the corpus. Finally, weretrain our NNER model iteratively over the augmented training set, includingthe seed, the reference-set examples, and the weakly-labeled examples, whichimproves model performance. We show empirically that this augmentedbootstrapping process significantly improves NER performance, and discuss thefactors impacting the efficacy of the approach.

Biomedical Named Entity Recognition via Reference-Set Augmented Bootstrapping