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).
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.
Zhou, Wangchunshu, Tao Ge, Ke Xu, Furu Wei, & Ming Zhou. (2019). BERT-based Lexical Substitution. 3368–3373.48 indexed citations
7.
Li, Minghao, Lei Cui, Shaohan Huang, et al.. (2019). TableBank: Table Benchmark for Image-based Table Detection and Recognition. arXiv (Cornell University). 1918–1925.33 indexed citations
8.
Yang, Yaodong, Rui Luo, Minne Li, et al.. (2018). Mean Field Multi-Agent Reinforcement Learning. UCL Discovery (University College London). 5571–5580.45 indexed citations
9.
Ge, Tao, Lei Cui, Baobao Chang, Zhifang Sui, & Ming Zhou. (2016). Event Detection with Burst Information Networks. International Conference on Computational Linguistics. 3276–3286.12 indexed citations
10.
Feng, Shi, Shujie Liu, Nan Yang, et al.. (2016). Improving Attention Modeling with Implicit Distortion and Fertility for Machine Translation. International Conference on Computational Linguistics. 3082–3092.15 indexed citations
Lee, Seung-Wook, Dongdong Zhang, Mu Li, Ming Zhou, & Hae‐Chang Rim. (2012). Translation Model Size Reduction for Hierarchical Phrase-based Statistical Machine Translation. Meeting of the Association for Computational Linguistics. 2. 291–295.3 indexed citations
14.
Liu, Shujie, Chi-Ho Li, Mu Li, & Ming Zhou. (2012). Learning Translation Consensus with Structured Label Propagation. Meeting of the Association for Computational Linguistics. 1. 302–310.9 indexed citations
15.
Liu, Shujie, Chi-Ho Li, & Ming Zhou. (2010). Discriminative Pruning for Discriminative ITG Alignment. Meeting of the Association for Computational Linguistics. 316–324.3 indexed citations
16.
Li, Mu, et al.. (2010). Adaptive Development Data Selection for Log-linear Model in Statistical Machine Translation. International Conference on Computational Linguistics. 662–670.14 indexed citations
17.
Zhao, Shiqi, Cheng Niu, Ming Zhou, Ting Liu, & Sheng Li. (2008). Combining Multiple Resources to Improve SMT-based Paraphrasing Model. Meeting of the Association for Computational Linguistics. 1021–1029.49 indexed citations
18.
Li, Mu, et al.. (2007). Improving Query Spelling Correction Using Web Search Results. Empirical Methods in Natural Language Processing. 49. 181–189.64 indexed citations
19.
Zhang, Dongdong, Mu Li, Chi-Ho Li, & Ming Zhou. (2007). Phrase Reordering Model Integrating Syntactic Knowledge for SMT. Empirical Methods in Natural Language Processing. 533–540.20 indexed citations
20.
Li, Chi-Ho, Minghui Li, Dongdong Zhang, et al.. (2007). A Probabilistic Approach to Syntax-based Reordering for Statistical Machine Translation. Meeting of the Association for Computational Linguistics. 720–727.62 indexed citations
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.