Community challenges in biomedical text mining over 10 years: success, failure and the future

One effective way to improve the state of the art is through competitions. Following the success of the Critical Assessment of protein Structure Prediction (CASP) in bioinformatics research, a number of challenge evaluations have been organized by the text-mining research community to assess and adv...

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Vydané v:Briefings in bioinformatics Ročník 17; číslo 1; s. 132 - 144
Hlavní autori: Huang, Chung-Chi, Lu, Zhiyong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England Oxford Publishing Limited (England) 01.01.2016
Oxford University Press
Predmet:
Algorithms
Bioinformatics
Biomedical research
Computational Biology - methods
Computational Biology - trends
Data mining
Data Mining - methods
Data Mining - trends
Databases, Genetic - statistics & numerical data
Humans
medicine
Natural Language Processing
prediction
protein structure
Proteins
stakeholders
critical assessment
BioNLP shared tasks
text mining
BioNLP challenges
biomedical natural language processing (BioNLP)
ISSN:1467-5463, 1477-4054, 1477-4054
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Shrnutí:One effective way to improve the state of the art is through competitions. Following the success of the Critical Assessment of protein Structure Prediction (CASP) in bioinformatics research, a number of challenge evaluations have been organized by the text-mining research community to assess and advance natural language processing (NLP) research for biomedicine. In this article, we review the different community challenge evaluations held from 2002 to 2014 and their respective tasks. Furthermore, we examine these challenge tasks through their targeted problems in NLP research and biomedical applications, respectively. Next, we describe the general workflow of organizing a Biomedical NLP (BioNLP) challenge and involved stakeholders (task organizers, task data producers, task participants and end users). Finally, we summarize the impact and contributions by taking into account different BioNLP challenges as a whole, followed by a discussion of their limitations and difficulties. We conclude with future trends in BioNLP challenge evaluations.
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ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bbv024