Using distant supervision to augment manually annotated data for relation extraction


This is the data for the paper "Using distant supervision to augment manually annotated data for relation extraction"

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.

Metadata Access
Creator Su, P.
Publisher Data Archiving and Networked Services (DANS)
Contributor Li, G.
Publication Year 2019
Rights info:eu-repo/semantics/openAccess;License:
Contact University of Delaware;Li, G.
Language English
Resource Type Dataset
Format application/pdf;text/plain
Discipline Not stated
Temporal Coverage Begin 2019-01-01T11:59:59Z
Temporal Coverage End 2019-04-25T11:59:59Z