research-article

AR-BERT: Aspect-relation enhanced Aspect-level Sentiment Classification with Multi-modal Explanations

Authors:

Sk Mainul Islam,

Sourangshu BhattacharyaAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 987 - 998

https://doi.org/10.1145/3485447.3511941

Published: 25 April 2022 Publication History

Abstract

Aspect level sentiment classification (ALSC) is a difficult problem with state-of-the-art models showing less than 80% macro-F1 score on benchmark datasets. Existing models do not incorporate information on aspect-aspect relations in knowledge graphs (KGs), e.g. DBpedia. Two main challenges stem from inaccurate disambiguation of aspects to KG entities, and the inability to learn aspect representations from the large KGs in joint training with ALSC models. We propose AR-BERT, a novel two-level global-local entity embedding scheme that allows efficient joint training of KG-based aspect embeddings and ALSC models. A novel incorrect disambiguation detection technique addresses the problem of inaccuracy in aspect disambiguation. We also introduce the problem of determining mode significance in multi-modal explanation generation, and propose a two step solution. The proposed methods show a consistent improvement of 2.5 − 4.1 percentage points, over the recent BERT-based baselines on benchmark datasets.

References

[1]

Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. 2008. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment 2008, 10 (Oct 2008), P10008. https://doi.org/10.1088/1742-5468/2008/10/p10008

[2]

Janez Brank, Gregor Leban, and Marko Grobelnik. 2018. Semantic Annotation of Documents Based on Wikipedia Concepts. Informatica (Slovenia) 42, 1 (2018). http://www.informatica.si/index.php/informatica/article/view/2228

[3]

Samuel Broscheit. 2019. Investigating Entity Knowledge in BERT with Simple Neural End-To-End Entity Linking. In Proceedings of the 23rd Conference on Computational Natural Language Learning, CoNLL 2019, Hong Kong, China, November 3-4, 2019, Mohit Bansal and Aline Villavicencio (Eds.). Association for Computational Linguistics, 677–685. https://doi.org/10.18653/v1/K19-1063

[4]

Peng Chen, Zhongqian Sun, Lidong Bing, and Wei Yang. 2017. Recurrent Attention Network on Memory for Aspect Sentiment Analysis. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, Copenhagen, Denmark, September 9-11, 2017, Martha Palmer, Rebecca Hwa, and Sebastian Riedel (Eds.). Association for Computational Linguistics, 452–461. https://doi.org/10.18653/v1/d17-1047

[5]

Junqi Dai, Hang Yan, Tianxiang Sun, Pengfei Liu, and Xipeng Qiu. 2021. Does syntax matter? A strong baseline for Aspect-based Sentiment Analysis with RoBERTa. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, June 6-11, 2021, Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tür, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, and Yichao Zhou (Eds.). Association for Computational Linguistics, 1816–1829. https://doi.org/10.18653/v1/2021.naacl-main.146

[6]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers), Jill Burstein, Christy Doran, and Thamar Solorio (Eds.). Association for Computational Linguistics, 4171–4186. https://doi.org/10.18653/v1/n19-1423

[7]

Li Dong, Furu Wei, Chuanqi Tan, Duyu Tang, Ming Zhou, and Ke Xu. 2014. Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classification. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014, June 22-27, 2014, Baltimore, MD, USA, Volume 2: Short Papers. The Association for Computer Linguistics, 49–54. https://doi.org/10.3115/v1/p14-2009

[8]

Chunning Du, Haifeng Sun, Jingyu Wang, Qi Qi, Jianxin Liao, Tong Xu, and Ming Liu. 2019. Capsule Network with Interactive Attention for Aspect-Level Sentiment Classification. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3-7, 2019, Kentaro Inui, Jing Jiang, Vincent Ng, and Xiaojun Wan (Eds.). Association for Computational Linguistics, 5488–5497. https://doi.org/10.18653/v1/D19-1551

[9]

William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.). 1024–1034. https://proceedings.neurips.cc/paper/2017/hash/5dd9db5e033da9c6fb5ba83c7a7ebea9-Abstract.html

[10]

John Hewitt and Christopher D. Manning. 2019. A Structural Probe for Finding Syntax in Word Representations. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers), Jill Burstein, Christy Doran, and Thamar Solorio (Eds.). Association for Computational Linguistics, 4129–4138. https://doi.org/10.18653 / v1 / n19-1419

[11]

Minqing Hu and Bing Liu. 2004. Mining and summarizing customer reviews. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, Washington, USA, August 22-25, 2004, Won Kim, Ron Kohavi, Johannes Gehrke, and William DuMouchel (Eds.). ACM, 168–177. https://doi.org/10.1145/1014052.1014073

Digital Library

[12]

Bin Jiang, Jing Hou, Wanyue Zhou, Chao Yang, Shihan Wang, and Liang Pang. 2020. METNet: A Mutual Enhanced Transformation Network for Aspect-based Sentiment Analysis. In Proceedings of the 28th International Conference on Computational Linguistics. 162–172.

[13]

Atsushi Kanehira, Kentaro Takemoto, Sho Inayoshi, and Tatsuya Harada. 2019. Multimodal Explanations by Predicting Counterfactuality in Videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[14]

Rijula Kar, Susmija Reddy, Sourangshu Bhattacharya, Anirban Dasgupta, and Soumen Chakrabarti. 2018. Task-Specific Representation Learning for Web-Scale Entity Disambiguation. 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, 2018, Sheila A. McIlraith and Kilian Q. Weinberger (Eds.). AAAI Press, 5812–5819. https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17281

[15]

Xin Li, Lidong Bing, Wai Lam, and Bei Shi. 2018. Transformation Networks for Target-Oriented Sentiment Classification. 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, 946–956. https://doi.org/10.18653/v1/P18-1087

[16]

Bin Liang, Rongdi Yin, Lin Gui, Jiachen Du, and Ruifeng Xu. 2020. Jointly Learning Aspect-Focused and Inter-Aspect Relations with Graph Convolutional Networks for Aspect Sentiment Analysis. In Proceedings of the 28th International Conference on Computational Linguistics. 150–161.

[17]

Jiangming Liu and Yue Zhang. 2017. Attention Modeling for Targeted Sentiment. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017, Valencia, Spain, April 3-7, 2017, Volume 2: Short Papers, Mirella Lapata, Phil Blunsom, and Alexander Koller (Eds.). Association for Computational Linguistics, 572–577. https://doi.org/10.18653/v1/e17-2091

[18]

Weijie Liu, Peng Zhou, Zhe Zhao, Zhiruo Wang, Qi Ju, Haotang Deng, and Ping Wang. 2020. K-bert: Enabling language representation with knowledge graph. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 2901–2908.

[19]

Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, and Xiang Zhang. 2020. Parameterized explainer for graph neural network. arXiv preprint arXiv:2011.04573(2020).

[20]

Dehong Ma, Sujian Li, Xiaodong Zhang, and Houfeng Wang. 2017. Interactive Attention Networks for Aspect-Level Sentiment Classification. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19-25, 2017, Carles Sierra (Ed.). ijcai.org, 4068–4074. https://doi.org/10.24963/ijcai.2017/568

[21]

Navonil Majumder, Soujanya Poria, Alexander Gelbukh, Md Shad Akhtar, Erik Cambria, and Asif Ekbal. 2018. IARM: Inter-aspect relation modeling with memory networks in aspect-based sentiment analysis. In Proceedings of the 2018 conference on empirical methods in natural language processing. 3402–3411.

[22]

Dong Huk Park, Lisa Anne Hendricks, Zeynep Akata, Anna Rohrbach, Bernt Schiele, Trevor Darrell, and Marcus Rohrbach. 2018. Multimodal explanations: Justifying decisions and pointing to the evidence. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 8779–8788.

[23]

Matthew E Peters, Mark Neumann, Robert Logan, Roy Schwartz, Vidur Joshi, Sameer Singh, and Noah A Smith. 2019. Knowledge Enhanced Contextual Word Representations. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 43–54.

[24]

Nina Poerner, Ulli Waltinger, and Hinrich Schütze. 2020. E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings. 803–818.

[25]

Maria Pontiki, Dimitris Galanis, John Pavlopoulos, Harris Papageorgiou, Ion Androutsopoulos, and Suresh Manandhar. 2014. SemEval-2014 Task 4: Aspect Based Sentiment Analysis. In Proceedings of the 8th International Workshop on Semantic Evaluation, SemEval@COLING 2014, Dublin, Ireland, August 23-24, 2014, Preslav Nakov and Torsten Zesch (Eds.). The Association for Computer Linguistics, 27–35. https://doi.org/10.3115/v1/s14-2004

[26]

Emily Reif, Ann Yuan, Martin Wattenberg, Fernanda B. Viégas, Andy Coenen, Adam Pearce, and Been Kim. 2019. Visualizing and Measuring the Geometry of BERT. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, 8-14 December 2019, Vancouver, BC, Canada, Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d’Alché-Buc, Emily B. Fox, and Roman Garnett (Eds.). 8592–8600. http://papers.nips.cc/paper/9065-visualizing-and-measuring-the-geometry-of-bert

[27]

Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. ”Why Should I Trust You?”: Explaining the Predictions of Any Classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13-17, 2016. 1135–1144.

Digital Library

[28]

Alexander Rietzler, Sebastian Stabinger, Paul Opitz, and Stefan Engl. 2020. Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classification. In Proceedings of The 12th Language Resources and Evaluation Conference, LREC 2020, Marseille, France, May 11-16, 2020, Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asunción Moreno, Jan Odijk, and Stelios Piperidis (Eds.). European Language Resources Association, 4933–4941. https://www.aclweb.org/anthology/2020.lrec-1.607/

[29]

Emanuel H Silva and Ricardo M Marcacini. [n. d.]. Aspect-based Sentiment Analysis using BERT with Disentangled Attention. ([n. d.]).

[30]

Chi Sun, Luyao Huang, and Xipeng Qiu. 2019. Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers), Jill Burstein, Christy Doran, and Thamar Solorio (Eds.). Association for Computational Linguistics, 380–385. https://doi.org/10.18653/v1/n19-1035

[31]

Tianxiang Sun, Yunfan Shao, Xipeng Qiu, Qipeng Guo, Yaru Hu, Xuan-Jing Huang, and Zheng Zhang. 2020. CoLAKE: Contextualized Language and Knowledge Embedding. In Proceedings of the 28th International Conference on Computational Linguistics. 3660–3670.

[32]

Duyu Tang, Bing Qin, and Ting Liu. 2016. Aspect Level Sentiment Classification with Deep Memory Network. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016, Austin, Texas, USA, November 1-4, 2016, Jian Su, Xavier Carreras, and Kevin Duh (Eds.). The Association for Computational Linguistics, 214–224. https://doi.org/10.18653/v1/d16-1021

[33]

Hao Tang, Donghong Ji, Chenliang Li, and Qiji Zhou. 2020. Dependency graph enhanced dual-transformer structure for aspect-based sentiment classification. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 6578–6588.

[34]

Jialong Tang, Ziyao Lu, Jinsong Su, Yubin Ge, Linfeng Song, Le Sun, and Jiebo Luo. 2019. Progressive Self-Supervised Attention Learning for Aspect-Level Sentiment Analysis. In Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers, Anna Korhonen, David R. Traum, and Lluís Màrquez (Eds.). Association for Computational Linguistics, 557–566. https://doi.org/10.18653/v1/p19-1053

[35]

Shuai Wang, Sahisnu Mazumder, Bing Liu, Mianwei Zhou, and Yi Chang. 2018. Target-Sensitive Memory Networks for Aspect Sentiment Classification. 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, 957–967. https://doi.org/10.18653/v1/P18-1088

[36]

Yequan Wang, Minlie Huang, Xiaoyan Zhu, and Li Zhao. 2016. Attention-based LSTM for Aspect-level Sentiment Classification. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016, Austin, Texas, USA, November 1-4, 2016, Jian Su, Xavier Carreras, and Kevin Duh (Eds.). The Association for Computational Linguistics, 606–615. https://doi.org/10.18653/v1/d16-1058

[37]

Bowen Xing and Ivor W. Tsang. 2021. Understand me, if you refer to Aspect Knowledge: Knowledge-aware Gated Recurrent Memory Network. CoRR abs/2108.02352(2021). arXiv:2108.02352https://arxiv.org/abs/2108.02352

[38]

Lu Xu, Lidong Bing, Wei Lu, and Fei Huang. 2020. Aspect Sentiment Classification with Aspect-Specific Opinion Spans. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020, Bonnie Webber, Trevor Cohn, Yulan He, and Yang Liu (Eds.). Association for Computational Linguistics, 3561–3567. https://doi.org/10.18653/v1/2020.emnlp-main.288

[39]

Heng Yang, Biqing Zeng, Mayi Xu, and Tianxing Wang. 2021. Back to Reality: Leveraging Pattern-driven Modeling to Enable Affordable Sentiment Dependency Learning. CoRR abs/2110.08604(2021). arXiv:2110.08604https://arxiv.org/abs/2110.08604

[40]

Heng Yang, Biqing Zeng, Jianhao Yang, Youwei Song, and Ruyang Xu. 2021. A multi-task learning model for Chinese-oriented aspect polarity classification and aspect term extraction. Neurocomputing 419(2021), 344–356. https://doi.org/10.1016/j.neucom.2020.08.001

[41]

Chih-Kuan Yeh, Been Kim, Sercan O Arik, Chun-Liang Li, Tomas Pfister, and Pradeep Ravikumar. 2019. On completeness-aware concept-based explanations in deep neural networks. arXiv preprint arXiv:1910.07969(2019).

[42]

Rex Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec. 2019. Gnnexplainer: Generating explanations for graph neural networks. Advances in neural information processing systems 32 (2019), 9240.

[43]

Chen Zhang, Qiuchi Li, and Dawei Song. 2019. Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 4560–4570.

[44]

Zhengyan Zhang, Xu Han, Zhiyuan Liu, Xin Jiang, Maosong Sun, and Qun Liu. 2019. ERNIE: Enhanced Language Representation with Informative Entities. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 1441–1451.

[45]

Pinlong Zhao, Linlin Hou, and Ou Wu. 2020. Modeling sentiment dependencies with graph convolutional networks for aspect-level sentiment classification. Knowl. Based Syst. 193(2020), 105443. https://doi.org/10.1016/j.knosys.2019.105443

Digital Library

Cited By

Zhang FZheng W(2023)Aspect-based Sentiment Analysis with External Knowledge Embedding and Syntactic InformationProceedings of the 2023 7th International Conference on Electronic Information Technology and Computer Engineering10.1145/3650400.3650472(439-444)Online publication date: 20-Oct-2023
https://dl.acm.org/doi/10.1145/3650400.3650472
Tang XZheng MFeng JHuang JGong Y(2023)Shortcut Enhanced Syntactic and Semantic Dual-channel Network for Aspect-based Sentiment AnalysisACM Transactions on Asian and Low-Resource Language Information Processing10.1145/362951822:11(1-20)Online publication date: 23-Oct-2023
https://dl.acm.org/doi/10.1145/3629518
Zhong QDing LLiu JDu BJin HTao D(2023)Knowledge Graph Augmented Network Towards Multiview Representation Learning for Aspect-Based Sentiment AnalysisIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.325049935:10(10098-10111)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1109/TKDE.2023.3250499
Show More Cited By

Index Terms

AR-BERT: Aspect-relation enhanced Aspect-level Sentiment Classification with Multi-modal Explanations
1. Applied computing
  1. Arts and humanities
    1. Language translation
2. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Published: 25 April 2022

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Cited By

Zhang FZheng W(2023)Aspect-based Sentiment Analysis with External Knowledge Embedding and Syntactic InformationProceedings of the 2023 7th International Conference on Electronic Information Technology and Computer Engineering10.1145/3650400.3650472(439-444)Online publication date: 20-Oct-2023
https://dl.acm.org/doi/10.1145/3650400.3650472
Tang XZheng MFeng JHuang JGong Y(2023)Shortcut Enhanced Syntactic and Semantic Dual-channel Network for Aspect-based Sentiment AnalysisACM Transactions on Asian and Low-Resource Language Information Processing10.1145/362951822:11(1-20)Online publication date: 23-Oct-2023
https://dl.acm.org/doi/10.1145/3629518
Zhong QDing LLiu JDu BJin HTao D(2023)Knowledge Graph Augmented Network Towards Multiview Representation Learning for Aspect-Based Sentiment AnalysisIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.325049935:10(10098-10111)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1109/TKDE.2023.3250499
Noorian AHarounabadi AHazratifard M(2023)A sequential neural recommendation system exploiting BERT and LSTM on social media postsComplex & Intelligent Systems10.1007/s40747-023-01191-410:1(721-744)Online publication date: 3-Aug-2023
https://doi.org/10.1007/s40747-023-01191-4
Chen BPeng WSong J(2022)ConAs-GRNs: Sentiment Classification with Construction-Assisted Multi-Scale Graph Reasoning NetworksElectronics10.3390/electronics1112182511:12(1825)Online publication date: 8-Jun-2022
https://doi.org/10.3390/electronics11121825
Liu CChen JWang TLiu QWu XMao L(2022)Aspect-Level Sentiment Classification Based on Self-Attention Routing via Capsule Network2022 IEEE Smartworld, Ubiquitous Intelligence & Computing, Scalable Computing & Communications, Digital Twin, Privacy Computing, Metaverse, Autonomous & Trusted Vehicles (SmartWorld/UIC/ScalCom/DigitalTwin/PriComp/Meta)10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00280(1936-1941)Online publication date: Dec-2022
https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00280

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