(Translated by https://www.hiragana.jp/)
MGICL: Multi-Grained Interaction Contrastive Learning for Multimodal Named Entity Recognition | Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

research-article

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

Authors:

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.

Supplementary Material

MP4 File (full2187-video.mp4)

Presentation video for CIKM2023-full2187paper

Download
38.56 MB

References

[1]

Meysam Asgari-Chenaghlu, Mohammad-Reza Feizi-Derakhshi, Leili Farzinvash, and Cina Motamed. 2020. A multimodal deep learning approach for named entity recognition from social media. CoRR, Vol. abs/2001.06888 (2020). showeprint[arXiv]2001.06888 https://arxiv.org/abs/2001.06888

[2]

Dawei Chen, Zhixu Li, Binbin Gu, and Zhigang Chen. 2021. Multimodal Named Entity Recognition with Image Attributes and Image Knowledge. In Database Systems for Advanced Applications - 26th International Conference, DASFAA 2021, Taipei, Taiwan, April 11--14, 2021, Proceedings, Part II (Lecture Notes in Computer Science, Vol. 12682), Christian S. Jensen, Ee-Peng Lim, De-Nian Yang, Wang-Chien Lee, Vincent S. Tseng, Vana Kalogeraki, Jen-Wei Huang, and Chih-Ya Shen (Eds.). Springer, 186--201. https://doi.org/10.1007/978--3-030--73197--7_12

[3]

Xiang Chen, Ningyu Zhang, Lei Li, Shumin Deng, Chuanqi Tan, Changliang Xu, Fei Huang, Luo Si, and Huajun Chen. 2022a. Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion. In SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11 - 15, 2022, Enrique Amigó, Pablo Castells, Julio Gonzalo, Ben Carterette, J. Shane Culpepper, and Gabriella Kazai (Eds.). ACM, 904--915. https://doi.org/10.1145/3477495.3531992

Digital Library

[4]

Xiang Chen, Ningyu Zhang, Lei Li, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, Luo Si, and Huajun Chen. 2022b. Good Visual Guidance Makes A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction. CoRR, Vol. abs/2205.03521 (2022). https://doi.org/10.48550/arXiv.2205.03521 showeprint[arXiv]2205.03521

[5]

Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmá n, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020. Unsupervised Cross-lingual Representation Learning at Scale. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5--10, 2020, Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel R. Tetreault (Eds.). Association for Computational Linguistics, 8440--8451. https://doi.org/10.18653/v1/2020.acl-main.747

[6]

Meihuizi Jia, Lei Shen, Xin Shen, Lejian Liao, Meng Chen, Xiaodong He, Zhendong Chen, and Jiaqi Li. 2022a. MNER-QG: An End-to-End MRC framework for Multimodal Named Entity Recognition with Query Grounding. CoRR, Vol. abs/2211.14739 (2022). https://doi.org/10.48550/arXiv.2211.14739 showeprint[arXiv]2211.14739

[7]

Meihuizi Jia, Xin Shen, Lei Shen, Jinhui Pang, Lejian Liao, Yang Song, Meng Chen, and Xiaodong He. 2022b. Query Prior Matters: A MRC Framework for Multimodal Named Entity Recognition. In MM '22: The 30th ACM International Conference on Multimedia, Lisboa, Portugal, October 10 - 14, 2022, Jo a o Magalh a es, Alberto Del Bimbo, Shin'ichi Satoh, Nicu Sebe, Xavier Alameda-Pineda, Qin Jin, Vincent Oria, and Laura Toni (Eds.). ACM, 3549--3558. https://doi.org/10.1145/3503161.3548427

Digital Library

[8]

Junnan Li, Ramprasaath R. Selvaraju, Akhilesh Gotmare, Shafiq R. Joty, Caiming Xiong, and Steven Chu-Hong Hoi. 2021. Align before Fuse: Vision and Language Representation Learning with Momentum Distillation. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6--14, 2021, virtual, Marc'Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, and Jennifer Wortman Vaughan (Eds.). 9694--9705. https://proceedings.neurips.cc/paper/2021/hash/505259756244493872b7709a8a01b536-Abstract.html

[9]

Luping Liu, Meiling Wang, Mozhi Zhang, Linbo Qing, and Xiaohai He. 2022. UAMNer: uncertainty-aware multimodal named entity recognition in social media posts. Appl. Intell., Vol. 52, 4 (2022), 4109--4125. https://doi.org/10.1007/s10489-021-02546--5

Digital Library

[10]

Di Lu, Leonardo Neves, Vitor Carvalho, Ning Zhang, and Heng Ji. 2018. Visual Attention Model for Name Tagging in Multimodal Social Media. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, July 15--20, 2018, Volume 1: Long Papers, Iryna Gurevych and Yusuke Miyao (Eds.). Association for Computational Linguistics, 1990--1999. https://doi.org/10.18653/v1/P18--1185

[11]

Junyu Lu, Dixiang Zhang, Jiaxing Zhang, and Pingjian Zhang. 2022. Flat Multi-modal Interaction Transformer for Named Entity Recognition. In Proceedings of the 29th International Conference on Computational Linguistics, COLING 2022, Gyeongju, Republic of Korea, October 12--17, 2022, Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, and Seung-Hoon Na (Eds.). International Committee on Computational Linguistics, 2055--2064. https://aclanthology.org/2022.coling-1.179

[12]

Yiwei Ma, Guohai Xu, Xiaoshuai Sun, Ming Yan, Ji Zhang, and Rongrong Ji. 2022. X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval. In MM '22: The 30th ACM International Conference on Multimedia, Lisboa, Portugal, October 10 - 14, 2022, Jo a o Magalh a es, Alberto Del Bimbo, Shin'ichi Satoh, Nicu Sebe, Xavier Alameda-Pineda, Qin Jin, Vincent Oria, and Laura Toni (Eds.). ACM, 638--647. https://doi.org/10.1145/3503161.3547910

Digital Library

[13]

Seungwhan Moon, Leonardo Neves, and Vitor Carvalho. 2018. Multimodal Named Entity Recognition for Short Social Media Posts. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2018, New Orleans, Louisiana, USA, June 1--6, 2018, Volume 1 (Long Papers), Marilyn A. Walker, Heng Ji, and Amanda Stent (Eds.). Association for Computational Linguistics, 852--860. https://doi.org/10.18653/v1/n18--1078

[14]

Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021. Learning Transferable Visual Models From Natural Language Supervision. In Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18--24 July 2021, Virtual Event (Proceedings of Machine Learning Research, Vol. 139), Marina Meila and Tong Zhang (Eds.). PMLR, 8748--8763. http://proceedings.mlr.press/v139/radford21a.html

[15]

R. Smith. 2007. An Overview of the Tesseract OCR Engine. In 9th International Conference on Document Analysis and Recognition (ICDAR 2007), 23--26 September, Curitiba, Paraná, Brazil. IEEE Computer Society, 629--633. https://doi.org/10.1109/ICDAR.2007.4376991

[16]

Lin Sun, Jiquan Wang, Yindu Su, Fangsheng Weng, Yuxuan Sun, Zengwei Zheng, and Yuanyi Chen. 2020. RIVA: A Pre-trained Tweet Multimodal Model Based on Text-image Relation for Multimodal NER. In Proceedings of the 28th International Conference on Computational Linguistics, COLING 2020, Barcelona, Spain (Online), December 8--13, 2020, Donia Scott, Nú ria Bel, and Chengqing Zong (Eds.). International Committee on Computational Linguistics, 1852--1862. https://doi.org/10.18653/v1/2020.coling-main.168

[17]

Lin Sun, Jiquan Wang, Kai Zhang, Yindu Su, and Fangsheng Weng. 2021. RpBERT: A Text-image Relation Propagation-based BERT Model for Multimodal NER. In Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2--9, 2021. AAAI Press, 13860--13868. https://ojs.aaai.org/index.php/AAAI/article/view/17633

[18]

Xinyu Wang, Jiong Cai, Yong Jiang, Pengjun Xie, Kewei Tu, and Wei Lu. 2022a. Named Entity and Relation Extraction with Multi-Modal Retrieval. In Findings of the Association for Computational Linguistics: EMNLP 2022, Abu Dhabi, United Arab Emirates, December 7--11, 2022, Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang (Eds.). Association for Computational Linguistics, 5925--5936. https://aclanthology.org/2022.findings-emnlp.437

[19]

Xinyu Wang, Min Gui, Yong Jiang, Zixia Jia, Nguyen Bach, Tao Wang, Zhongqiang Huang, and Kewei Tu. 2022b. ITA: Image-Text Alignments for Multi-Modal Named Entity Recognition. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2022, Seattle, WA, United States, July 10--15, 2022, Marine Carpuat, Marie-Catherine de Marneffe, and Ivá n Vladimir Meza Ru'i z (Eds.). Association for Computational Linguistics, 3176--3189. https://doi.org/10.18653/v1/2022.naacl-main.232

[20]

Xuwu Wang, Junfeng Tian, Min Gui, Zhixu Li, Jiabo Ye, Ming Yan, and Yanghua Xiao. 2022c. PromptMNER: Prompt-Based Entity-Related Visual Clue Extraction and Integration for Multimodal Named Entity Recognition. In Database Systems for Advanced Applications - 27th International Conference, DASFAA 2022, Virtual Event, April 11--14, 2022, Proceedings, Part III (Lecture Notes in Computer Science, Vol. 13247), Arnab Bhattacharya, Janice Lee, Mong Li, Divyakant Agrawal, P. Krishna Reddy, Mukesh K. Mohania, Anirban Mondal, Vikram Goyal, and Rage Uday Kiran (Eds.). Springer, 297--305. https://doi.org/10.1007/978--3-031-00129--1_24

[21]

Xuwu Wang, Jiabo Ye, Zhixu Li, Junfeng Tian, Yong Jiang, Ming Yan, Ji Zhang, and Yanghua Xiao. 2022d. CAT-MNER: Multimodal Named Entity Recognition with Knowledge-Refined Cross-Modal Attention. In IEEE International Conference on Multimedia and Expo, ICME 2022, Taipei, Taiwan, July 18--22, 2022. IEEE, 1--6. https://doi.org/10.1109/ICME52920.2022.9859972

[22]

Zhiwei Wu, Changmeng Zheng, Yi Cai, Junying Chen, Ho-fung Leung, and Qing Li. 2020. Multimodal Representation with Embedded Visual Guiding Objects for Named Entity Recognition in Social Media Posts. In MM '20: The 28th ACM International Conference on Multimedia, Virtual Event / Seattle, WA, USA, October 12--16, 2020, Chang Wen Chen, Rita Cucchiara, Xian-Sheng Hua, Guo-Jun Qi, Elisa Ricci, Zhengyou Zhang, and Roger Zimmermann (Eds.). ACM, 1038--1046. https://doi.org/10.1145/3394171.3413650

Digital Library

[23]

Bo Xu, Shizhou Huang, Chaofeng Sha, and Hongya Wang. 2022. MAF: A General Matching and Alignment Framework for Multimodal Named Entity Recognition. In WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining, Virtual Event / Tempe, AZ, USA, February 21 - 25, 2022, K. Selcuk Candan, Huan Liu, Leman Akoglu, Xin Luna Dong, and Jiliang Tang (Eds.). ACM, 1215--1223. https://doi.org/10.1145/3488560.3498475

Digital Library

[24]

Jianwei Yang, Yonatan Bisk, and Jianfeng Gao. 2021. TACo: Token-aware Cascade Contrastive Learning for Video-Text Alignment. In 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, October 10--17, 2021. IEEE, 11542--11552. https://doi.org/10.1109/ICCV48922.2021.01136

[25]

Lewei Yao, Runhui Huang, Lu Hou, Guansong Lu, Minzhe Niu, Hang Xu, Xiaodan Liang, Zhenguo Li, Xin Jiang, and Chunjing Xu. 2022. FILIP: Fine-grained Interactive Language-Image Pre-Training. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25--29, 2022. OpenReview.net. https://openreview.net/forum?id=cpDhcsEDC2

[26]

Jianfei Yu, Jing Jiang, Li Yang, and Rui Xia. 2020. Improving Multimodal Named Entity Recognition via Entity Span Detection with Unified Multimodal Transformer. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5--10, 2020, Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel R. Tetreault (Eds.). Association for Computational Linguistics, 3342--3352. https://doi.org/10.18653/v1/2020.acl-main.306

[27]

Dong Zhang, Suzhong Wei, Shoushan Li, Hanqian Wu, Qiaoming Zhu, and Guodong Zhou. 2021b. Multi-modal Graph Fusion for Named Entity Recognition with Targeted Visual Guidance. In Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2--9, 2021. AAAI Press, 14347--14355. https://ojs.aaai.org/index.php/AAAI/article/view/17687

[28]

Pengchuan Zhang, Xiujun Li, Xiaowei Hu, Jianwei Yang, Lei Zhang, Lijuan Wang, Yejin Choi, and Jianfeng Gao. 2021a. VinVL: Revisiting Visual Representations in Vision-Language Models. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, virtual, June 19--25, 2021. Computer Vision Foundation / IEEE, 5579--5588. https://doi.org/10.1109/CVPR46437.2021.00553

[29]

Qi Zhang, Jinlan Fu, Xiaoyu Liu, and Xuanjing Huang. 2018. Adaptive Co-attention Network for Named Entity Recognition in Tweets. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2--7, 2019, Sheila A. McIlraith and Kilian Q. Weinberger (Eds.). AAAI Press, 5674--5681. https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16432

[30]

Fei Zhao, Chunhui Li, Zhen Wu, Shangyu Xing, and Xinyu Dai. 2022. Learning from Different text-image Pairs: A Relation-enhanced Graph Convolutional Network for Multimodal NER. In MM '22: The 30th ACM International Conference on Multimedia, Lisboa, Portugal, October 10 - 14, 2022, Jo a o Magalh a es, Alberto Del Bimbo, Shin'ichi Satoh, Nicu Sebe, Xavier Alameda-Pineda, Qin Jin, Vincent Oria, and Laura Toni (Eds.). ACM, 3983--3992. https://doi.org/10.1145/3503161.3548228

Digital Library

[31]

Changmeng Zheng, Zhiwei Wu, Tao Wang, Yi Cai, and Qing Li. 2020. Object-aware multimodal named entity recognition in social media posts with adversarial learning. IEEE Transactions on Multimedia, Vol. 23 (2020), 2520--2532.

Digital Library

[32]

Xiangru Zhu, Zhixu Li, Xiaodan Wang, Xueyao Jiang, Penglei Sun, Xuwu Wang, Yanghua Xiao, and Nicholas Jing Yuan. 2022. Multi-Modal Knowledge Graph Construction and Application: A Survey. CoRR, Vol. abs/2202.05786 (2022). showeprint[arXiv]2202.05786 https://arxiv.org/abs/2202.05786

Digital Library

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

Recommendations

Reducing the Bias of Visual Objects in Multimodal Named Entity Recognition
WSDM '23: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining

Visual information shows to empower accurately named entity recognition in short texts, such as posts from social media. Previous work on multimodal named entity recognition (MNER) often regards an image as a set of visual objects, trying to explicitly ...
Fine-Grained Multimodal Named Entity Recognition and Grounding with a Generative Framework
MM '23: Proceedings of the 31st ACM International Conference on Multimedia

Multimodal Named Entity Recognition (MNER) aims to locate and classify named entities mentioned in a pair of text and image. However, most previous MNER works focus on extracting entities in the form of text but failing to ground text symbols to their ...
MPMRC-MNER: A Unified MRC framework for Multimodal Named Entity Recognition based Multimodal Prompt
CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

Multimodal named entity recognition (MNER) is a vision-language task, which aims to detect entity spans and classify them to corresponding entity types given a sentence-image pair. Existing methods often regard an image as a set of visual objects, trying ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

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

October 2023

5508 pages

ISBN:9798400701245

DOI:10.1145/3583780

General Chairs:
Ingo Frommholz
University of Wolverhampton, UK
,
Frank Hopfgartner
University of Koblenz, Germany
,
Mark Lee
University of Birmingham, UK
,
Michael Oakes
University of Birmingham, UK
,
Program Chairs:
Mounia Lalmas
Spotify, UK
,
Min Zhang
Tsinghua University, China
,
Rodrygo Santos
Federal University of Minas Gerais, Brazil

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 October 2023

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Key R&D Program of China
NSFC

Conference

CIKM '23

Sponsor:

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

October 21 - 25, 2023

Birmingham, United Kingdom

Acceptance Rates

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
306
Total Downloads

Downloads (Last 12 months)306
Downloads (Last 6 weeks)9

Reflects downloads up to 11 Sep 2024

Other Metrics

View Author Metrics

Citations

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

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents