GRAM-CNN: a deep learning approach with local context for named entity recognition in biomedical text-Reference-Cited by-同舟云学术

GRAM-CNN: a deep learning approach with local context for named entity recognition in biomedical text

Published:2017-12-20 Issue:9 Volume:34 Page:1547-1554
ISSN:1367-4803
Container-title:Bioinformatics
language:en
Short-container-title:

Author:

Zhu Qile¹²,Li Xiaolin¹³,Conesa Ana⁴⁵,Pereira Cécile⁴

Affiliation:

1. National Science Foundation Center for Big Learning, University of Florida, Gainesville, FL, USA

2. Department of Computer & Information Science & Engineering, University of Florida, Gainesville, FL, USA

3. Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA

4. Department of Microbiology and Cell Science, Institute for Food and Agricultural Sciences, University of Florida, Gainesville, FL, USA

5. Genomics of Gene Expression Laboratory, Centro de Investigación Príncipe Felipe, Valencia, Spain

Funder

National Institute of Food and Agriculture

Department of Agriculture

National Science Foundation

National Institutes of Health

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Link

http://academic.oup.com/bioinformatics/article-pdf/34/9/1547/25120965/btx815.pdf

Reference40 articles.

1. Gimli: open source and high-performance biomedical name recognition;Campos;BMC Bioinformatics,2013

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