# Biomedical Named Entity Recognition via Reference-Set Augmented Bootstrapping

arXiv: https://arxiv.org/abs/1906.00282 · HTML: https://mlsanity.com/paper/1906.00282/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2019.

## Built on by (most cited first, 4 of 4)

- [A Robust and Domain-Adaptive Approach for Low-Resource Named Entity Recognition](https://mlsanity.com/paper/2101.00388/) (2021)
- [A Multilingual Evaluation of NER Robustness to Adversarial Inputs](https://mlsanity.com/paper/2305.18933/) (2023)
- [Federated Named Entity Recognition](https://mlsanity.com/paper/2203.15101/) (2022)
- [Computational Approaches to Arabic-English Code-Switching](https://mlsanity.com/paper/2410.13318/) (2024)

## Abstract (arXiv)

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.