2026 Lidong Bing: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	42	8289	8053	3553	3418	195	8857

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

959
Citations
Transformation Networks for Target-Oriented Sentiment Classification

Xin Li;Lidong Bing;Wai Lam;Bei Shi

487
Citations
Knowing What, How and Why: A Near Complete Solution for Aspect-Based Sentiment Analysis

Haiyun Peng;Lu Xu;Lidong Bing;Fei Huang

431
Citations
A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges

Unknown

411
Citations
Exploiting BERT for End-to-End Aspect-based Sentiment Analysis.

Xin Li;Lidong Bing;Wenxuan Zhang;Wai Lam

344
Citations
A Unified Model for Opinion Target Extraction and Target Sentiment Prediction

Xin Li;Lidong Bing;Piji Li;Wai Lam

333
Citations
Neural Rating Regression with Abstractive Tips Generation for Recommendation

Piji Li;Zihao Wang;Zhaochun Ren;Lidong Bing

289
Citations
Position-Aware Tagging for Aspect Sentiment Triplet Extraction

Lu Xu;Hao Li;Wei Lu;Lidong Bing

270
Citations
Aspect Term Extraction with History Attention and Selective Transformation

Xin Li;Lidong Bing;Piji Li;Wai Lam

259
Citations
Deep Recurrent Generative Decoder for Abstractive Text Summarization

Piji Li;Wai Lam;Lidong Bing;Zihao Wang

229
Citations
Abstractive Multi-Document Summarization via Phrase Selection and Merging

Lidong Bing;Piji Li;Yi Liao;Wai Lam

184
Citations
Aspect Sentiment Quad Prediction as Paraphrase Generation

Wenxuan Zhang;Yang Deng;Xin Li;Yifei Yuan

183
Citations
Towards Generative Aspect-Based Sentiment Analysis

Wenxuan Zhang;Xin Li;Yang Deng;Lidong Bing

177
Citations
Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction

Lu Xu;Yew Ken Chia;Lidong Bing

172
Citations
DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks

Bosheng Ding;Linlin Liu;Lidong Bing;Canasai Kruengkrai

137
Citations
On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation

Ruidan He;Linlin Liu;Hai Ye;Qingyu Tan

129
Citations
MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER

Unknown

123
Citations
An Unsupervised Sentence Embedding Method by Mutual Information Maximization

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

122
Citations
Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning

Zheng Li;Xin Li;Ying Wei;Lidong Bing

97
Citations
Sentiment Analysis in the Era of Large Language Models: A Reality Check

Unknown

94
Citations
Salience Estimation via Variational Auto-Encoders for Multi-Document Summarization

Piji Li;Zihao Wang;Wai Lam;Zhaochun Ren

65
Citations
MulDA: A Multilingual Data Augmentation Framework for Low-Resource Cross-Lingual NER

Linlin Liu;Bosheng Ding;Lidong Bing;Shafiq Joty

52
Citations
Generating Distractors for Reading Comprehension Questions from Real Examinations

Yifan Gao;Lidong Bing;Piji Li;Irwin King

49
Citations

Frequent Co-Authors

Wai Lam Chinese University of Hong Kong

Luo Si Alibaba Group (China)

William W. Cohen Carnegie Mellon University

Rui Yan Renmin University of China

Irwin King Chinese University of Hong Kong

Dongyan Zhao Peking University

Michael R. Lyu Chinese University of Hong Kong

Shafiq Joty Salesforce (United States)

Shuming Shi Tencent (China)

Hwee Tou Ng National University of Singapore

External Links

Personal website of Lidong Bing

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