research-article

MGICL: Multi-Grained Interaction Contrastive Learning for Multimodal Named Entity Recognition

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Weidong XiaoAuthors Info & Claims

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

Pages 639 - 648

https://doi.org/10.1145/3583780.3614967

Published: 21 October 2023 Publication History

Abstract

Multimodal Named Entity Recognition (MNER) aims to combine data from different modalities (e.g. text, images, videos, etc.) for recognition and classification of named entities, which is crucial for constructing Multimodal Knowledge Graphs (MMKGs). However, existing researches suffer from two prominant issues: over-reliance on textual features while neglecting visual features, and the lack of effective reduction of the feature space discrepancy of multimodal data. To overcome these challenges, this paper proposes a Multi-Grained Interaction Contrastive Learning framework for MNER task, namely MGICL. MGICL slices data into different granularities, i.e., sentence level/word token level for text, and image level/object level for image. By utilizing multimodal features with different granularities, the framework enables cross-contrast and narrows down the feature space discrepancy between modalities. Moreover, it facilitates the acquisition of valuable visual features by the text. Additionally, a visual gate control mechanism is introduced to dynamically select relevant visual information, thereby reducing the impact of visual noise. Experimental results demonstrate that the proposed MGICL framework satisfactorily tackles the challenges of MNER through enhancing information interaction of multimodal data and reducing the effect of noise, and hence, effectively improves the performance of MNER.

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Cited By

Zhao TMeng LSong D(2024)Multimodal Aspect-Based Sentiment Analysis: A survey of tasks, methods, challenges and future directionsInformation Fusion10.1016/j.inffus.2024.102552112(102552)Online publication date: Dec-2024
https://doi.org/10.1016/j.inffus.2024.102552
Tong ZLiu QShi HXia YWu SZhang X(2024)Semantics Fusion of Hierarchical Transformers for Multimodal Named Entity RecognitionAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5669-8_34(414-426)Online publication date: 3-Aug-2024
https://doi.org/10.1007/978-981-97-5669-8_34

Index Terms

MGICL: Multi-Grained Interaction Contrastive Learning for Multimodal Named Entity Recognition
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
    2. Natural language processing
      1. Information extraction

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CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

October 2023

5508 pages

ISBN:9798400701245

DOI:10.1145/3583780

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University of Wolverhampton, UK
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Program Chairs:
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Spotify, UK
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Tsinghua University, China
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Federal University of Minas Gerais, Brazil

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Published: 21 October 2023

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CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management

October 21 - 25, 2023

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Cited By

Zhao TMeng LSong D(2024)Multimodal Aspect-Based Sentiment Analysis: A survey of tasks, methods, challenges and future directionsInformation Fusion10.1016/j.inffus.2024.102552112(102552)Online publication date: Dec-2024
https://doi.org/10.1016/j.inffus.2024.102552
Tong ZLiu QShi HXia YWu SZhang X(2024)Semantics Fusion of Hierarchical Transformers for Multimodal Named Entity RecognitionAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5669-8_34(414-426)Online publication date: 3-Aug-2024
https://doi.org/10.1007/978-981-97-5669-8_34

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