DUTIR in BioNLP-ST 2016: Utilizing Convolutional Network and Distributed Representation to Extract Complicate Relations

Honglei Li, Jianhai Zhang, Jian Wang, Hongfei Lin, Zhihao Yang
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引用次数: 12

Abstract

We participate in the two event extraction tasks of BioNLP 2016 Shared Task: binary relation extraction of SeeDev task and localization relations extraction of Bacteria Biotope task. Convolutional neural network (CNN) is employed to model the sentences by convolution and maxpooling operation from raw input with word embedding. Then, full connected neural network is used to learn senior and significant features automatically. The proposed model mainly contains two modules: distributive semantic representation building, such as word embedding, POS embedding, distance embedding and entity type embedding, and CNN model training. The results with F-score of 0.370 and 0.478 in our participant tasks, which were evaluated on the test data set, show that our proposed method contributes to binary relation extraction effectively and can reduce the impact of artificial feature engineering through automatically feature learning.
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BioNLP-ST 2016中的DUTIR:利用卷积网络和分布式表示提取复杂关系
我们参与了BioNLP 2016共享任务的两个事件提取任务:SeeDev任务的二元关系提取和Bacteria Biotope任务的定位关系提取。利用卷积神经网络(CNN)对原始输入进行卷积和最大池化操作,并结合词嵌入对句子进行建模。然后,利用全连接神经网络自动学习高级特征和重要特征。该模型主要包含两个模块:分布式语义表示构建,如词嵌入、POS嵌入、距离嵌入和实体类型嵌入;CNN模型训练。在测试数据集上对我们的参与者任务进行评估,f值分别为0.370和0.478,结果表明我们的方法可以有效地提取二元关系,并且可以通过自动特征学习减少人工特征工程的影响。
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