Distant Supervision for Relation Extraction with Hierarchical Selective Attention
Zhou, Peng; Xu, Jiaming; Qi, Zhenyu; Bao, Hongyun; Chen, Zhineng; Xu, Bo
2018
发表期刊NEURAL NETWORKS
期号108页码:240-247
摘要Distant supervised relation extraction is an important task in the field of natural language processing. There are two main shortcomings for most state-of-the-art methods. One is that they take all sentences of an entity pair as input, which would result in a large computational cost. But in fact, few of most relevant sentences are enough to recognize the relation of an entity pair. To tackle these problems, we propose a novel hierarchical selective attention network for relation extraction under distant supervision. Our model first selects most relevant sentences by taking coarse sentence-level attention on all sentences
of an entity pair and then employs word-level attention to construct sentence representations and fine sentence-level attention to aggregate these sentence representations. Experimental results on a widely used dataset demonstrate that our method performs significantly better than most of existing methods.
关键词Relation Extraction Distant Supervision Hierarchical Attention Piecewise Convolutional Neural Networks
收录类别SCI
语种英语
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/21759
专题数字内容技术与服务研究中心_听觉模型与认知计算
推荐引用方式
GB/T 7714
Zhou, Peng,Xu, Jiaming,Qi, Zhenyu,et al. Distant Supervision for Relation Extraction with Hierarchical Selective Attention[J]. NEURAL NETWORKS,2018(108):240-247.
APA Zhou, Peng,Xu, Jiaming,Qi, Zhenyu,Bao, Hongyun,Chen, Zhineng,&Xu, Bo.(2018).Distant Supervision for Relation Extraction with Hierarchical Selective Attention.NEURAL NETWORKS(108),240-247.
MLA Zhou, Peng,et al."Distant Supervision for Relation Extraction with Hierarchical Selective Attention".NEURAL NETWORKS .108(2018):240-247.
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