Ming Zhou

27.6k total citations · 9 hit papers
211 papers, 12.8k citations indexed

About

Ming Zhou is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Ming Zhou has authored 211 papers receiving a total of 12.8k indexed citations (citations by other indexed papers that have themselves been cited), including 192 papers in Artificial Intelligence, 36 papers in Computer Vision and Pattern Recognition and 35 papers in Information Systems. Recurrent topics in Ming Zhou's work include Topic Modeling (178 papers), Natural Language Processing Techniques (148 papers) and Multimodal Machine Learning Applications (27 papers). Ming Zhou is often cited by papers focused on Topic Modeling (178 papers), Natural Language Processing Techniques (148 papers) and Multimodal Machine Learning Applications (27 papers). Ming Zhou collaborates with scholars based in China, United States and United Kingdom. Ming Zhou's co-authors include Furu Wei, Duyu Tang, Nan Duan, Nan Yang, Bing Qin, Ting Liu, Xiaohua Liu, Daya Guo, Shujie Liu and Long Jiang and has published in prestigious journals such as SHILAP Revista de lepidopterología, Frontiers in Plant Science and ACM Computing Surveys.

In The Last Decade

Ming Zhou

207 papers receiving 12.1k citations

Hit Papers

5 papers align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

CodeBERT: A Pre-Trained Model for Programming and Natural Languages

2020 1.4k citations Duyu Tang, Nan Duan et al. profile →
Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification

2014 781 citations Duyu Tang, Furu Wei et al. profile →
Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classification

2014 708 citations Li Dong, Furu Wei et al. profile →
Target-dependent Twitter Sentiment Classification

2011 578 citations Ming Zhou et al. profile →
Gated Self-Matching Networks for Reading Comprehension and Question Answering

2017 360 citations Furu Wei, Ming Zhou et al. profile →
UniXcoder: Unified Cross-Modal Pre-training for Code Representation

2022 302 citations Nan Duan, Ming Zhou et al. profile →
GraphCodeBERT: Pre-training Code Representations with Data Flow

2021 261 citations Duyu Tang, Shujie Liu et al. profile →
MIND: A Large-scale Dataset for News Recommendation

2020 238 citations Xing Xie, Ming Zhou et al. profile →
A Survey of Controllable Text Generation Using Transformer-based Pre-trained Language Models

2023 128 citations Ming Zhou et al. profile →

Peers

Countries citing papers authored by Ming Zhou

Since Specialization

Citations

This map shows the geographic impact of Ming Zhou's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ming Zhou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ming Zhou more than expected).

Fields of papers citing papers by Ming Zhou

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ming Zhou. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ming Zhou. The network helps show where Ming Zhou may publish in the future.

Co-authorship network of co-authors of Ming Zhou

This figure shows the co-authorship network connecting the top 25 collaborators of Ming Zhou. A scholar is included among the top collaborators of Ming Zhou based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ming Zhou. Ming Zhou is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

#	Work	Indexed citations
1	A Hybrid Detection and Generation Framework with Separate Encoders for Event Extraction 2023 ·Ge Shi, (unknown), Yongliang Ma, Ming Zhou	1
2	LogiQA 2.0—An Improved Dataset for Logical Reasoning in Natural Language Understanding 2023 ·IEEE/ACM Transactions on Audio Speech and Language Processing ·(unknown), Liu Jian, Leyang Cui, Zhiyang Teng, Nan Duan, Ming Zhou, Yue Zhang	8
3	Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text 2022 ·Findings of the Association for Computational Linguistics: ACL 2022 ·Siyuan Wang, Wanjun Zhong, Duyu Tang, Zhongyu Wei, Zhihao Fan, Daxin Jiang, Ming Zhou, Nan Duan	16
4	Compare to The Knowledge: Graph Neural Fake News Detection with External Knowledge 2021 ·Linmei Hu, Tianchi Yang, (unknown), Wanjun Zhong, Duyu Tang, Chuan Shi, Nan Duan, Ming Zhou	120
5	Improving Grammatical Error Correction with Machine Translation Pairs 2020 ·Wangchunshu Zhou, Tao Ge, (unknown), Ke Xu, Furu Wei, Ming Zhou	22
6	BERT-of-Theseus: Compressing BERT by Progressive Module Replacing 2020 ·Canwen Xu, Wangchunshu Zhou, Tao Ge, Furu Wei, Ming Zhou	100
7	Graph Neural News Recommendation with Unsupervised Preference Disentanglement 2020 ·Linmei Hu, (unknown), Chen Li, Cheng Yang, Chuan Shi, Nan Duan, Xing Xie, Ming Zhou	82
8	Neural Deepfake Detection with Factual Structure of Text 2020 ·Wanjun Zhong, Duyu Tang, (unknown), Ruize Wang, Nan Duan, Ming Zhou, Jiahai Wang, Jian Yin	25
9	ProphetNet: Predicting Future N-gram for Sequence-to-SequencePre-training 2020 ·Weizhen Qi, Yu Yan, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang, Ming Zhou	199
10	Reasoning Over Semantic-Level Graph for Fact Checking 2020 ·Wanjun Zhong, Jingjing Xu, Duyu Tang, (unknown), Nan Duan, Ming Zhou, Jiahai Wang, Jian Yin	100
11	Neural Latent Extractive Document Summarization 2018 ·Edinburgh Research Explorer (University of Edinburgh) ·Xingxing Zhang, Mirella Lapata, Furu Wei, Ming Zhou	98
12	SuperAgent: A Customer Service Chatbot for E-commerce Websites 2017 ·Lei Cui, Shaohan Huang, Furu Wei, Chuanqi Tan, Chaoqun Duan, Ming Zhou	194
13	Learning to Generate Product Reviews from Attributes 2017 ·Edinburgh Research Explorer ·Li Dong, Shaohan Huang, Furu Wei, Mirella Lapata, Ming Zhou, Ke Xu	115
14	Question Generation for Question Answering 2017 ·Nan Duan, Duyu Tang, Peng Chen, Ming Zhou	163
15	DocChat: An Information Retrieval Approach for Chatbot Engines Using Unstructured Documents 2016 ·Yan Zhao, Nan Duan, Junwei Bao, (unknown), Ming Zhou, Zhoujun Li, (unknown)	72
16	Hierarchical Recurrent Neural Network for Document Modeling 2015 ·Rui Lin, Shujie Liu, Muyun Yang, Mu Li, Ming Zhou, Sheng Li	99
17	Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classification breakdown → 2014 ·Li Dong, Furu Wei, Chuanqi Tan, Duyu Tang, Ming Zhou, Ke Xu	708
18	Efficient Collective Entity Linking with Stacking 2013 ·(unknown), Shujie Liu, Yang Song, Mu Li, Ming Zhou, Houfeng Wang	15
19	Learning question paraphrases for QA from Encarta logs 2007 ·International Joint Conference on Artificial Intelligence ·Shiqi Zhao, Ming Zhou, Ting Liu	19
20	Identifying synonyms among distributionally similar words 2003 ·International Joint Conference on Artificial Intelligence ·Dekang Lin, Shaojun Zhao, (unknown), Ming Zhou	117

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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