Self-Supervised Enhancement for Named Entity Disambiguation via Multimodal Graph Convolution
文献类型：期刊论文
| 作者 | Zhou, Pengfei1,2,3; Ying, Kaining2; Wang, Zhenhua2; Guo, Dongyan2; Bai, Cong2 |
| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
 |
| 出版日期 | 2022-05-13 |
| 页码 | 15 |
| 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) |
| ISSN号 | 2162-237X |
| DOI | 10.1109/TNNLS.2022.3173179 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000798360300001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/19550]  |
| 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.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China 2.Zhejiang Univ Technol, Coll Comp Sci & Technol, Hangzhou 310023, Zhejiang, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, 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. |
入库方式： OAI收割
来源：计算技术研究所
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