Self-Supervised Enhancement for Named Entity Disambiguation via Multimodal Graph Convolution

doi:10.1109/TNNLS.2022.3173179

	Self-Supervised Enhancement for Named Entity Disambiguation via Multimodal Graph Convolution
	Zhou, Pengfei 1,2,3; Ying, Kaining 1; Wang, Zhenhua 1; Guo, Dongyan 1; Bai, Cong 1
	2022-05-13
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
页码	15
摘要	Named entity disambiguation (NED) finds the specific meaning of an entity mention in a particular context and links it to a target entity. With the emergence of multimedia, the modalities of content on the Internet have become more diverse, which poses difficulties for traditional NED, and the vast amounts of information make it impossible to manually label every kind of ambiguous data to train a practical NED model. In response to this situation, we present MMGraph, which uses multimodal graph convolution to aggregate visual and contextual language information for accurate entity disambiguation for short texts, and a self-supervised simple triplet network (SimTri) that can learn useful representations in multimodal unlabeled data to enhance the effectiveness of NED models. We evaluated these approaches on a new dataset, MMFi, which contains multimodal supervised data and large amounts of unlabeled data. Our experiments confirm the state-of-the-art performance of MMGraph on two widely used benchmarks and MMFi. SimTri further improves the performance of NED methods. The dataset and code are available at https://github.com/LanceZPF/NNED_MMGraph.
关键词	Task analysis Convolution Semantics Internet Bit error rate Visualization Pipelines Graph convolutional network (GCN) multimodal data named entity disambiguation (NED) self-supervised learning (SSL)
DOI	10.1109/TNNLS.2022.3173179
收录类别	SCI
语种	英语
资助项目	Zhejiang Provincial Natural Science Foundation of China[LR21F020002] ; Zhejiang Provincial Natural Science Foundation of China[LY21F020024] ; Zhejiang Provincial Natural Science Foundation of China[LY22F030015] ; Natural Science Foundation of China[U20A20196]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000798360300001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：5[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/2XEOYT63/19551
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Bai, Cong
作者单位	1.Zhejiang Univ Technol, Coll Comp Sci & Technol, Hangzhou 310023, Zhejiang, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Zhou, Pengfei,Ying, Kaining,Wang, Zhenhua,et al. Self-Supervised Enhancement for Named Entity Disambiguation via Multimodal Graph Convolution[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2022:15.
APA	Zhou, Pengfei,Ying, Kaining,Wang, Zhenhua,Guo, Dongyan,&Bai, Cong.(2022).Self-Supervised Enhancement for Named Entity Disambiguation via Multimodal Graph Convolution.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,15.
MLA	Zhou, Pengfei,et al."Self-Supervised Enhancement for Named Entity Disambiguation via Multimodal Graph Convolution".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022):15.