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.

本文提出了一种弱监督数据增强方法，用于提高命名实体识别在具有挑战性的领域中的表现，结果表明该方法显著提高了 NER 性能，同时讨论了影响该方法有效性的因素。