World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
42
Citations
8857
World Ranking
8288
National Ranking
3553

Lidong Bing publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Lidong Bing sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 195 publications — 44th percentile

44% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Lidong Bing D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Lidong Bing sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 42 D-Index — 43rd percentile

43% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Lidong Bing is a researcher affiliated with Carnegie Mellon University in the United States. Their work predominantly lies within the broad field of Computer Science, with a focus on various subfields such as Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Management Science and Operations Research, and Computer Networks and Communications.

Their primary research topics include:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Sentiment Analysis and Opinion Mining
  • Multimodal Machine Learning Applications
  • Advanced Text Analysis Techniques
  • Text Readability and Simplification
  • Text and Document Classification Technologies

Lidong Bing has contributed extensively to the academic literature, with publications appearing frequently in venues such as:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Findings of the Association for Computational Linguistics: ACL 2022
  • Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  • Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Among their recent papers are:

  • "Knowing What, How and Why: A Near Complete Solution for Aspect-Based Sentiment Analysis" (2020), Proceedings of the AAAI Conference on Artificial Intelligence
  • "A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges" (2022), IEEE Transactions on Knowledge and Data Engineering
  • "Aspect Sentiment Quad Prediction as Paraphrase Generation" (2021), Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • "Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation" (2022), Findings of the Association for Computational Linguistics: ACL 2022
  • "On the Effectiveness of Parameter-Efficient Fine-Tuning" (2023), Proceedings of the AAAI Conference on Artificial Intelligence

Their frequent research collaborators include Luo Si, Wenxuan Zhang, Wai Lam, Yew Ken Chia, and Shafiq Joty.

Best Publications

  • Recurrent Attention Network on Memory for Aspect Sentiment Analysis

    Peng Chen;Zhongqian Sun;Lidong Bing;Wei Yang

  • Transformation Networks for Target-Oriented Sentiment Classification

    Xin Li;Lidong Bing;Wai Lam;Bei Shi

  • Knowing What, How and Why: A Near Complete Solution for Aspect-Based Sentiment Analysis

    Haiyun Peng;Lu Xu;Lidong Bing;Fei Huang

  • A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges

    Unknown

  • Exploiting BERT for End-to-End Aspect-based Sentiment Analysis.

    Xin Li;Lidong Bing;Wenxuan Zhang;Wai Lam

  • A Unified Model for Opinion Target Extraction and Target Sentiment Prediction

    Xin Li;Lidong Bing;Piji Li;Wai Lam

  • Neural Rating Regression with Abstractive Tips Generation for Recommendation

    Piji Li;Zihao Wang;Zhaochun Ren;Lidong Bing

  • Position-Aware Tagging for Aspect Sentiment Triplet Extraction

    Lu Xu;Hao Li;Wei Lu;Lidong Bing

  • Aspect Term Extraction with History Attention and Selective Transformation

    Xin Li;Lidong Bing;Piji Li;Wai Lam

  • Deep Recurrent Generative Decoder for Abstractive Text Summarization

    Piji Li;Wai Lam;Lidong Bing;Zihao Wang

  • Abstractive Multi-Document Summarization via Phrase Selection and Merging

    Lidong Bing;Piji Li;Yi Liao;Wai Lam

  • Aspect Sentiment Quad Prediction as Paraphrase Generation

    Wenxuan Zhang;Yang Deng;Xin Li;Yifei Yuan

  • Towards Generative Aspect-Based Sentiment Analysis

    Wenxuan Zhang;Xin Li;Yang Deng;Lidong Bing

  • Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction

    Lu Xu;Yew Ken Chia;Lidong Bing

  • DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks

    Bosheng Ding;Linlin Liu;Lidong Bing;Canasai Kruengkrai

  • On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation

    Ruidan He;Linlin Liu;Hai Ye;Qingyu Tan

  • MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER

    Unknown

  • An Unsupervised Sentence Embedding Method by Mutual Information Maximization

    Yan Zhang;Ruidan He;Zuozhu Liu;Kwan Hui Lim

  • Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning

    Zheng Li;Xin Li;Ying Wei;Lidong Bing

  • Sentiment Analysis in the Era of Large Language Models: A Reality Check

    Unknown

  • Salience Estimation via Variational Auto-Encoders for Multi-Document Summarization

    Piji Li;Zihao Wang;Wai Lam;Zhaochun Ren

  • MulDA: A Multilingual Data Augmentation Framework for Low-Resource Cross-Lingual NER

    Linlin Liu;Bosheng Ding;Lidong Bing;Shafiq Joty

  • Generating Distractors for Reading Comprehension Questions from Real Examinations

    Yifan Gao;Lidong Bing;Piji Li;Irwin King

Frequent Co-Authors

Wai Lam
Wai Lam Chinese University of Hong Kong
Luo Si
Luo Si Alibaba Group (China)
William W. Cohen
William W. Cohen Carnegie Mellon University
Rui Yan
Rui Yan Renmin University of China
Irwin King
Irwin King Chinese University of Hong Kong
Dongyan Zhao
Dongyan Zhao Peking University
Michael R. Lyu
Michael R. Lyu Chinese University of Hong Kong
Shafiq Joty
Shafiq Joty Salesforce (United States)
Shuming Shi
Shuming Shi Tencent (China)
Hwee Tou Ng
Hwee Tou Ng National University of Singapore

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