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Chemical-induced disease relation extraction via attention-based distant supervision.
- Source :
-
BMC Bioinformatics . 7/22/2019, Vol. 20 Issue 1, pN.PAG-N.PAG. 1p. 4 Diagrams, 9 Charts, 1 Graph. - Publication Year :
- 2019
-
Abstract
- Background: Automatically understanding chemical-disease relations (CDRs) is crucial in various areas of biomedical research and health care. Supervised machine learning provides a feasible solution to automatically extract relations between biomedical entities from scientific literature, its success, however, heavily depends on large-scale biomedical corpora manually annotated with intensive labor and tremendous investment. Results: We present an attention-based distant supervision paradigm for the BioCreative-V CDR extraction task. Training examples at both intra- and inter-sentence levels are generated automatically from the Comparative Toxicogenomics Database (CTD) without any human intervention. An attention-based neural network and a stacked auto-encoder network are applied respectively to induce learning models and extract relations at both levels. After merging the results of both levels, the document-level CDRs can be finally extracted. It achieves the precision/recall/F1-score of 60.3%/73.8%/66.4%, outperforming the state-of-the-art supervised learning systems without using any annotated corpus. Conclusion: Our experiments demonstrate that distant supervision is promising for extracting chemical disease relations from biomedical literature, and capturing both local and global attention features simultaneously is effective in attention-based distantly supervised learning. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14712105
- Volume :
- 20
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- BMC Bioinformatics
- Publication Type :
- Academic Journal
- Accession number :
- 137641822
- Full Text :
- https://doi.org/10.1186/s12859-019-2884-4