Community challenges in biomedical text mining over 10 years: success, failure and the future
Community challenges in biomedical text mining over 10 years: success, failure and the future
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DOI：
10.1093/bib/bbv024
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发表时间：
2016-01-01
影响因子：
9.5
通讯作者：
Lu, Zhiyong
中科院分区：
文献类型：
--
作者：
Huang, Chung-Chi;Lu, Zhiyong
提高艺术水平的一个有效方法是通过比赛。在生物信息学研究中的蛋白质结构预测关键评估（CASP）成功之后，文本挖掘研究社区组织了一些挑战性评估，以评估和推进生物医学的自然语言处理（NLP）研究。在本文中，我们回顾了2002年至2014年举行的不同社区挑战评估及其各自的任务。此外，我们通过NLP研究和生物医学应用中的目标问题来研究这些挑战性任务。接下来，我们描述了组织生物医学NLP（BioNLP）挑战的一般工作流程和相关利益相关者（任务组织者，任务数据生产者，任务参与者和最终用户）。最后，我们总结的影响和贡献，考虑到不同的BioNLP的挑战作为一个整体，其次是他们的局限性和困难的讨论。最后，我们总结了BioNLP挑战评估的未来趋势。
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.