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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Veröffentlicht in:Briefings in bioinformatics Jg. 17; H. 1; S. 132 - 144
Hauptverfasser: Huang, Chung-Chi, Lu, Zhiyong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Oxford Publishing Limited (England) 01.01.2016
Oxford University Press
Schlagworte:
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
Online-Zugang:Volltext
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Abstract 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.
AbstractList 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.
Author Lu, Zhiyong
Huang, Chung-Chi
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/25935162$$D View this record in MEDLINE/PubMed
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Copyright Published by Oxford University Press 2015. This work is written by US Government employees and is in the public domain in the US.
Copyright Oxford Publishing Limited(England) Jan 2016
Published by Oxford University Press 2015. This work is written by US Government employees and is in the public domain in the US. 2015
Copyright_xml – notice: Published by Oxford University Press 2015. This work is written by US Government employees and is in the public domain in the US.
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– notice: Published by Oxford University Press 2015. This work is written by US Government employees and is in the public domain in the US. 2015
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Issue 1
Keywords critical assessment
BioNLP shared tasks
text mining
BioNLP challenges
biomedical natural language processing (BioNLP)
Language English
License Published by Oxford University Press 2015. This work is written by US Government employees and is in the public domain in the US.
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Snippet One effective way to improve the state of the art is through competitions. Following the success of the Critical Assessment of protein Structure Prediction...
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StartPage 132
SubjectTerms 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
Title Community challenges in biomedical text mining over 10 years: success, failure and the future
URI https://www.ncbi.nlm.nih.gov/pubmed/25935162
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https://www.proquest.com/docview/1760910763
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https://pubmed.ncbi.nlm.nih.gov/PMC4719069
Volume 17
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