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个人信息Personal Information
教授
博士生导师
硕士生导师
性别:女
毕业院校:大连理工大学
学位:博士
所在单位:计算机科学与技术学院
学科:计算机应用技术. 计算机软件与理论
办公地点:创新大厦A930
电子邮箱:lils@dlut.edu.cn
Biomedical event extraction based on GRU integrating attention mechanism
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论文类型:期刊论文
发表时间:2018-08-13
发表刊物:BMC BIOINFORMATICS
收录刊物:PubMed、SCIE、CPCI-S
卷号:19
期号:Suppl 9
页面范围:285
ISSN号:1471-2105
关键字:Biomedical event extraction; Attention mechanism; Word representation; Deep learning
摘要:Background: Biomedical event extraction is a crucial task in biomedical text mining. As the primary forum for international evaluation of different biomedical event extraction technologies, BioNLP Shared Task represents a trend in biomedical text mining toward fine-grained information extraction (IE). The fourth series of BioNLP Shared Task in 2016 (BioNLP-ST'16) proposed three tasks, in which the Bacteria Biotope event extraction (BB) task has been put forward in the earlier BioNLP-ST. Deep learning methods provide an effective way to automatically extract more complex features and achieve notable results in various natural language processing tasks.
Results: The experimental results show that the presented approach can achieve an F-score of 57.42% in the test set, which outperforms previous state-of-the-art official submissions to BioNLP-ST 2016.
Conclusions: In this paper, we propose a novel Gated Recurrent Unit Networks framework integrating attention mechanism for extracting biomedical events between biotope and bacteria from biomedical literature, utilizing the corpus from the BioNLP'16 Shared Task on Bacteria Biotope task. The experimental results demonstrate the potential and effectiveness of the proposed framework.