RT Journal Article SR Electronic T1 Using distant supervision to augment manually annotated data for relation extraction JF bioRxiv FD Cold Spring Harbor Laboratory SP 626226 DO 10.1101/626226 A1 Peng Su A1 Gang Li A1 Cathy Wu A1 K. Vijay-Shanker YR 2019 UL http://biorxiv.org/content/early/2019/05/02/626226.abstract AB Significant progress has been made in applying deep learning on natural language processing tasks recently. However, deep learning models typically require a large amount of annotated training data while often only small labeled datasets are available for many natural language processing tasks in biomedical literature. Building large-size datasets for deep learning is expensive since it involves considerable human effort and usually requires domain expertise in specialized fields. In this work, we consider augmenting manually annotated data with large amounts of data using distant supervision. However, data obtained by distant supervision is often noisy, we first apply some heuristics to remove some of the incorrect annotations. Then using methods inspired from transfer learning, we show that the resulting models outperform models trained on the original manually annotated sets.