  We present a weakly-supervised data augmentation approach to improve Named
Entity Recognition (NER) in a challenging domain: extracting biomedical
entities (e.g., proteins) from the scientific literature. First, we train a
neural NER (NNER) model over a small seed of fully-labeled examples. Second, we
use a reference set of entity names (e.g., proteins in UniProt) to identify
entity 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, we
retrain our NNER model iteratively over the augmented training set, including
the seed, the reference-set examples, and the weakly-labeled examples, which
improves model performance. We show empirically that this augmented
bootstrapping process significantly improves NER performance, and discuss the
factors impacting the efficacy of the approach.
