Nested relation extraction with iterative neural network

doi:10.1007/s11704-020-9420-6

	Nested relation extraction with iterative neural network
	Cao, Yixuan 1,2; Chen, Dian 1,2; Xu, Zhengqi 1,2; Li, Hongwei 1,2; Luo, Ping 1,2
	2021-01-16
发表期刊	FRONTIERS OF COMPUTER SCIENCE
ISSN	2095-2228
卷号	15 期号:3 页码:14
摘要	Most existing researches on relation extraction focus on binary flat relations like BornIn relation between a Person and a Location. But a large portion of objective facts described in natural language are complex, especially in professional documents in fields such as finance and biomedicine that require precise expressions. For example, "the GDP of the United States in 2018 grew 2.9% compared with 2017" describes a growth rate relation between two other relations about the economic index, which is beyond the expressive power of binary flat relations. Thus, we propose the nested relation extraction problem and formulate it as a directed acyclic graph (DAG) structure extraction problem. Then, we propose a solution using the Iterative Neural Network which extracts relations layer by layer. The proposed solution achieves 78.98 and 97.89 F1 scores on two nested relation extraction tasks, namely semantic cause-and-effect relation extraction and formula extraction. Furthermore, we observe that nested relations are usually expressed in long sentences where entities are mentioned repetitively, which makes the annotation difficult and error-prone. Hence, we extend our model to incorporate a mention-insensitive mode that only requires annotations of relations on entity concepts (instead of exact mentions) while preserving most of its performance. Our mention-insensitive model performs better than the mention sensitive model when the random level in mention selection is higher than 0.3.
关键词	nested relation extraction mention insensitive relation iterative neural network
DOI	10.1007/s11704-020-9420-6
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2017YFB1002104] ; National Natural Science Foundation of China[U1811461] ; Innovation Program of Institute of Computing Technology, CAS
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS记录号	WOS:000610455900003
出版者	HIGHER EDUCATION PRESS
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://119.78.100.204/handle/2XEOYT63/16331
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Luo, Ping
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Cao, Yixuan,Chen, Dian,Xu, Zhengqi,et al. Nested relation extraction with iterative neural network[J]. FRONTIERS OF COMPUTER SCIENCE,2021,15(3):14.
APA	Cao, Yixuan,Chen, Dian,Xu, Zhengqi,Li, Hongwei,&Luo, Ping.(2021).Nested relation extraction with iterative neural network.FRONTIERS OF COMPUTER SCIENCE,15(3),14.
MLA	Cao, Yixuan,et al."Nested relation extraction with iterative neural network".FRONTIERS OF COMPUTER SCIENCE 15.3(2021):14.