通过卷积神经网络进行化学诱导疾病关系提取
Chemical-induced disease relation extraction via convolutional neural network.
作者信息
Gu Jinghang, Sun Fuqing, Qian Longhua, Zhou Guodong
机构信息
School of Computer Science and Technology, Soochow University, 1 Shizi Street, Suzhou, China.
Department of Gynecology Minimally Invasive Center, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, 17 Qihelou Street, Beijing, China.
出版信息
Database (Oxford). 2017 Jan 1;2017(1). doi: 10.1093/database/bax024.
UNLABELLED
This article describes our work on the BioCreative-V chemical-disease relation (CDR) extraction task, which employed a maximum entropy (ME) model and a convolutional neural network model for relation extraction at inter- and intra-sentence level, respectively. In our work, relation extraction between entity concepts in documents was simplified to relation extraction between entity mentions. We first constructed pairs of chemical and disease mentions as relation instances for training and testing stages, then we trained and applied the ME model and the convolutional neural network model for inter- and intra-sentence level, respectively. Finally, we merged the classification results from mention level to document level to acquire the final relations between chemical and disease concepts. The evaluation on the BioCreative-V CDR corpus shows the effectiveness of our proposed approach.
DATABASE URL
http://www.biocreative.org/resources/corpora/biocreative-v-cdr-corpus/.
未标注
本文介绍了我们在生物创意V化学-疾病关系（CDR）提取任务上的工作，该任务分别使用最大熵（ME）模型和卷积神经网络模型在句子间和句子内层面进行关系提取。在我们的工作中，文档中实体概念之间的关系提取被简化为实体提及之间的关系提取。我们首先构建化学和疾病提及对作为训练和测试阶段的关系实例，然后分别在句子间和句子内层面训练并应用ME模型和卷积神经网络模型。最后，我们将提及层面的分类结果合并到文档层面，以获取化学和疾病概念之间的最终关系。对生物创意V CDR语料库的评估表明了我们所提方法的有效性。
数据库网址
http://www.biocreative.org/resources/corpora/biocreative-v-cdr-corpus/ 。
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