App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 11

Multimodal named entity recognition method based on dual coding theory

Feng Guanga
Liao Beirongb
Huang Junhuib
Lin Yibaob
Sun Xianglib
Wu Zelinb
Zhu Xuanb
a. School of Automation, b. School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China
DOI: 10.19734/j.issn.1001-3695.2026.03.0088

Abstract

To address semantic sparsity and insufficient cross-modal correlation in social media multimodal named entity recognition (MNER) , this study proposes a novel entity recognition model inspired by dual-coding theory. First, a multi-source semantic enhancement module incorporates image captions and pre-trained word vectors to achieve preliminary semantic enhancement for both textual and visual features. Next, the model designs a verbal logic reconstruction system and a non-verbal reference system; the former extracts high-order textual features embedded with visual context, while the latter employs fine-grained alignment sampling to capture text-guided visual evidence. Concurrently, an adaptive visual refinement strategy based on a Mixture-of-Experts (MoE) network dynamically filters crucial visual evidence according to the context to suppress noise interference. Finally, the model integrates the features from both systems and utilizes a Conditional Random Field (CRF) to perform final sequence labeling. Experimental results on the Twitter-2015 and Twitter-2017 datasets demonstrate that the proposed model significantly outperforms existing mainstream methods in core evaluation metrics, such as the F1-score. This study confirms the effectiveness of dual-coding and dynamic refinement strategies in multimodal feature fusion.

Foundation Support

国家自然科学基金重点项目(62237001)
广东省哲学社会科学青年项目(GD23YJY08)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.03.0088
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 11

Publish History

[2026-07-17] Accepted Paper

Cite This Article

冯广, 廖贝融, 黄俊辉, 等. 基于双路编码理论的多模态命名实体识别方法 [J]. 计算机应用研究, 2026, 43 (11). (2026-07-31). https://doi.org/10.19734/j.issn.1001-3695.2026.03.0088. (Feng Guang, Liao Beirong, Huang Junhui, et al. Multimodal named entity recognition method based on dual coding theory [J]. Application Research of Computers, 2026, 43 (11). (2026-07-31). https://doi.org/10.19734/j.issn.1001-3695.2026.03.0088. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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