Frontiers of biomedical text mining: current progress

被引:149
作者
Zweigenbaum, Pierre
Demner-Fushman, Dina
Yu, Hong
Cohen, Kevin B.
机构
[1] CNRS, Comp Sci Lab Mech & Engn Sci, Language Informat & Representat Grp, F-91403 Orsay, France
[2] Univ Wisconsin, Dept Comp Sci, Milwaukee, WI 53201 USA
[3] Univ Wisconsin, Dept Hlth Sci, Milwaukee, WI 53201 USA
[4] Univ Colorado, Biomed Text Min Grp, Sch Med Ctr Computat Pharmacol, Boulder, CO 80309 USA
关键词
text mining; natural language processing; information extraction; text summarization; image mining; question answering; literature-based discovery; evaluation; user orientation;
D O I
10.1093/bib/bbm045
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
It is now almost 15 years since the publication of the first paper on text mining in the genomics domain, and decades since the first paper on text mining in the medical domain. Enormous progress has been made in the areas of information retrieval, evaluation methodologies and resource construction. Some problems, such as abbreviation-handling, can essentially be considered solved problems, and others, such as identification of gene mentions in text, seem likely to be solved soon. However, a number of problems at the frontiers of biomedical text mining continue to present interesting challenges and opportunities for great improvements and interesting research. In this article we review the current state of the art in biomedical text mining or BioNLP in general, focusing primarily on papers published within the past year.
引用
收藏
页码:358 / 375
页数:18
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