App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 6

Textual-visual semantics-enhanced multimodal named entity recognition method

Xu Xi1
Wang Hairong1,2
Wang Tong1
Ma He1
1. College of Computer Science & Engineering, North Minzu University, Yinchuan 750021, China
2. The Key Laboratory of Images & Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan 750021, China
DOI: 10.19734/j.issn.1001-3695.2023.09.0439

Abstract

To address the issues of missing textual-visual semantics and unclear multimodal representation semantics in multimodal named entity recognition methods, a method of textual-visual semantic enhancement for multimodal named entity recognition is proposed. In this method, various pre-trained models are used to extract text features, character features, regional visual features, image keywords, and visual labels, in order to comprehensively describe the semantic information of image-text data. The Transformer and cross-modal attention mechanism are adopted to mine the complementary semantic relationships between image-text features, guiding feature fusion, thereby generating semantically complete text representations and semantically enhanced multimodal representations. By integrating boundary detection, entity type detection, and named entity recognition tasks, a multi-task label decoder is constructed. This decoder can perform fine-grained semantic decoding of input features to improve the semantic accuracy of predicted features. This decoder is used to jointly decode text representations and multimodal representations to obtain globally optimal predicted labels. A large number of experimental results on the Twitter-2015 and Twitter-2017 benchmark datasets show that this method has increased the average F1 score by 1.00% and 1.41% respectively, this indicates that the model has a strong capability for named entity recognition.

Foundation Support

宁夏自然科学基金资助项目(2023AAC03316)
北方民族大学校级科研项目(2021XYZJK06)
北方民族大学中央高校基本科研业务费专项资金资助项目(2022PT_S04)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.09.0439
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 6

Publish History

[2023-12-19] Accepted Paper

Cite This Article

徐玺, 王海荣, 王彤, 等. 图文语义增强的多模态命名实体识别方法 [J]. 计算机应用研究, 2024, 41 (6). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.09.0439. (Xu Xi, Wang Hairong, Wang Tong, et al. Textual-visual semantics-enhanced multimodal named entity recognition method [J]. Application Research of Computers, 2024, 41 (6). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.09.0439. )

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.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)