Biomedical Named Entity Recognition via Reference-Set Augmented Bootstrapping
This work addresses the challenge of Named Entity Recognition in the biomedical domain, where labeled data is scarce, by providing an incremental method to enhance model accuracy.
The paper tackles the problem of extracting biomedical entities from scientific literature by proposing a weakly-supervised data augmentation approach that combines a neural NER model with a reference set of entity names and iterative bootstrapping, resulting in significant improvements in NER performance.
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.