Utilizing Entity-Based Gated Convolution and Multilevel Sentence Attention to Improve Distantly Supervised Relation Extraction

doi:10.1155/2021/6110885

CASIA OpenIR > 数字内容技术与服务研究中心 > 版权智能与文化计算

	Utilizing Entity-Based Gated Convolution and Multilevel Sentence Attention to Improve Distantly Supervised Relation Extraction
	Yi, Qian 1,2; Zhang, Guixuan1,2 ; Zhang, Shuwu1,2
发表期刊	COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
ISSN	1687-5265
	2021-11-01
卷号	2021 页码:10
通讯作者	Zhang, Shuwu(shuwu.zhang@ia.ac.cn)
摘要	Distant supervision is an effective method to automatically collect large-scale datasets for relation extraction (RE). Automatically constructed datasets usually comprise two types of noise: the intrasentence noise and the wrongly labeled noisy sentence. To address issues caused by the above two types of noise and improve distantly supervised relation extraction, this paper proposes a novel distantly supervised relation extraction model, which consists of an entity-based gated convolution sentence encoder and a multilevel sentence selective attention (Matt) module. Specifically, we first apply an entity-based gated convolution operation to force the sentence encoder to extract entity-pair-related features and filter out useless intrasentence noise information. Furthermore, the multilevel attention schema fuses the bag information to obtain a fine-grained bag-specific query vector, which can better identify valid sentences and reduce the influence of wrongly labeled sentences. Experimental results on a large-scale benchmark dataset show that our model can effectively reduce the influence of the above two types of noise and achieves state-of-the-art performance in relation extraction.
DOI	10.1155/2021/6110885
收录类别	SCI
语种	英语
WOS研究方向	Mathematical & Computational Biology ; Neurosciences & Neurology
WOS类目	Mathematical & Computational Biology ; Neurosciences
WOS记录号	WOS:000730816200001
出版者	HINDAWI LTD
七大方向——子方向分类	自然语言处理
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/46789
专题	数字内容技术与服务研究中心_版权智能与文化计算
通讯作者	Zhang, Shuwu
作者单位	1.Chinese Acad Sci, Inst Automat, Beijing Engn Res Ctr Digital Content Technol, Beijing 100010, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100010, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Yi, Qian,Zhang, Guixuan,Zhang, Shuwu. Utilizing Entity-Based Gated Convolution and Multilevel Sentence Attention to Improve Distantly Supervised Relation Extraction[J]. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE,2021,2021:10.
APA	Yi, Qian,Zhang, Guixuan,&Zhang, Shuwu.(2021).Utilizing Entity-Based Gated Convolution and Multilevel Sentence Attention to Improve Distantly Supervised Relation Extraction.COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE,2021,10.
MLA	Yi, Qian,et al."Utilizing Entity-Based Gated Convolution and Multilevel Sentence Attention to Improve Distantly Supervised Relation Extraction".COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021(2021):10.