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Bacterial Named Entity Recognition based on Dictionary and Conditional Random Field

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成果类型:
期刊论文、会议论文
作者:
Wang, Xiaoyan;Jiang, Xingpeng(蒋兴鹏);Liu, Mengwen;He, Tingting(何婷婷);Hu, Xiaohua*
通讯作者:
Hu, Xiaohua
作者机构:
[Jiang, Xingpeng; He, Tingting; Hu, Xiaohua; Wang, Xiaoyan] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.
[Hu, Xiaohua; Liu, Mengwen] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
通讯机构:
[Hu, Xiaohua] C
[Hu, Xiaohua] D
Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.
Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
语种:
英文
关键词:
conditional random field;microbial interaction;named entity recognition;text mining
期刊:
2017 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM)
ISSN:
2156-1125
年:
2017
卷:
2017-January
页码:
439-444
会议名称:
IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM)
会议论文集名称:
IEEE International Conference on Bioinformatics and Biomedicine-BIBM
会议时间:
NOV 13-16, 2017
会议地点:
Kansas City, MI
会议主办单位:
[Wang, Xiaoyan;Jiang, Xingpeng;He, Tingting;Hu, Xiaohua] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.^[Liu, Mengwen;Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
会议赞助商:
IEEE, IEEE Comp Soc, IEEE Tech Comm Computat Life Sci
主编:
Hu, XH Shyu, CR Bromberg, Y Gao, J Gong, Y Korkin, D Yoo, I Zheng, JH
出版地:
345 E 47TH ST, NEW YORK, NY 10017 USA
出版者:
IEEE
ISBN:
978-1-5090-3050-7
基金类别:
National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61532008]; National Key Research and Development Program of China [2017YFC0909502]; International Cooperation Project of Hubei Province [2014BHE0017]; Self-determined Research Funds of CCNU from the Colleges' Basic Research and Operation of MOE [CCNU17TS0003, CCNU16JYKX018]
机构署名:
本校为第一且通讯机构
院系归属:
计算机学院
摘要:
There are intensive computational efforts to discover large-scale microbial interactions from metagenomic abundance data, however, it is often difficult to validate such inferred interactions without a manually curated dataset. There are also a number of small-scale microbial interactions reported in massive literature with experimental confidence. Text mining can be employed to extract such microbial interactions from biomedical literature which could be a significant complement to abundance-based method. The key tasks of text mining include n...

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