Weakly supervised learning of biomedical information extraction from curated data

Background Numerous publicly available biomedical databases derive data by curating from literatures. The curated data can be useful as training examples for information extraction, but curated data usually lack the exact mentions and their locations in the text required for supervised machine learn...

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Veröffentlicht in:	BMC bioinformatics Jg. 17; H. Suppl 1; S. 1
Hauptverfasser:	Jain, Suvir, R., Kashyap, Kuo, Tsung-Ting, Bhargava, Shitij, Lin, Gordon, Hsu, Chun-Nan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 11.01.2016 BioMed Central Ltd
Schlagworte:	Abstracting and Indexing as Topic - methods Algorithms Bioinformatics Biomedical and Life Sciences Computational Biology/Bioinformatics Computational linguistics Computer Appl. in Life Sciences Data Curation Data mining Data Mining - methods Databases, Factual Disease - genetics Genetic Predisposition to Disease Genome-Wide Association Study Genomics Humans Language processing Life Sciences Machine learning Microarrays Natural language interfaces Proceedings Risk Assessment New York Biomedical text mining Database curation Natural language processing Information extraction Machine learning
ISSN:	1471-2105, 1471-2105
Online-Zugang:	Volltext
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