Extracting chemical reactions from text using Snorkel
Abstract
Enzymatic and chemical reactions are key for understanding biological processes in cells. Curated databases of chemical reactions exist but these databases struggle to keep up with the exponential growth of the biomedical literature. Conventional text mining pipelines provide tools to automatically extract entities and relationships from the scientific literature, and partially replace expert curation, but such machine learning frameworks often require a large amount of labeled training data and thus lack scalability for both larger document corpora and new relationship types.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- May 27, 2020
- Source ID
- 10.1186/s12859-020-03542-1
Entities
People
- Alex Ratner
- Ambika Acharya
- Christopher Ré
- Emily K. Mallory
- Matthieu De Rochemonteix
- Roselie A. Bright
- Russ Altman
Organizations
- Defense Advanced Research Projects Agency
- Food and Drug Administration
- National Institutes of Health
- National Science Foundation
- Office of Naval Research