Chemical-induced disease relation extraction via convolutional neural network.
Chemical-induced disease relation extraction via convolutional neural network.
复制标题
通过卷积神经网络提取化学引起的疾病关系
DOI：
10.1093/database/bax024
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发表时间：
2017-01-01
期刊：
影响因子：
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通讯作者：
Zhou G
中科院分区：
文献类型：
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作者：
Gu J;Sun F;Qian L;Zhou G
本文描述了我们在BioCreative-V化学疾病关系（CDR）提取任务上的工作，该任务分别采用最大熵（ME）模型和卷积神经网络模型进行句间和句内级别的关系提取。本文将文档中实体概念之间的关系抽取简化为实体提及之间的关系抽取。我们首先构建了成对的化学和疾病提及作为训练和测试阶段的关系实例，然后我们分别在句间和句内水平训练和应用ME模型和卷积神经网络模型。最后，我们合并的分类结果，从提到的水平，文件的水平，以获得化学和疾病的概念之间的最终关系。在BioCreative-V CDR语料库上的测试结果表明了该方法的有效性。 数据库URL：http://www.biocreative.org/resources/corpora/biocreative-v-cdr-corpus/
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/