Zexuan Zhong
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 5%
- Information Systems top 5%
- Molecular Biology
- Management Science and Operations Research top 10%
- Topics
- Topic Modeling (13 papers)Natural Language Processing Techniques (10 papers)Multimodal Machine Learning Applications (3 papers)
- Journals
- 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 ProcessingNational Conference on Artificial Intelligence
- Partner nations
- United StatesChinaUnited Kingdom
In The Last Decade
Zexuan Zhong
16 papers receiving 829 citations
Hit Papers
Peers
Comparison fields: 5 of 85
- Artificial Intelligence 698
- Computer Vision and Pattern Recognition 179
- Information Systems 118
- Molecular Biology 71
- Management Science and Operations Research 71
Countries citing papers authored by Zexuan Zhong
This map shows the geographic impact of Zexuan Zhong'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 Zexuan Zhong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zexuan Zhong more than expected).
Fields of papers citing papers by Zexuan Zhong
This network shows the impact of papers produced by Zexuan Zhong. 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 Zexuan Zhong. The network helps show where Zexuan Zhong may publish in the future.
Co-authorship network of co-authors of Zexuan Zhong
This figure shows the co-authorship network connecting the top 25 collaborators of Zexuan Zhong. A scholar is included among the top collaborators of Zexuan Zhong 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 Zexuan Zhong. Zexuan Zhong is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 8 | |
| 2 | 60 | |
| 3 | 10 | |
| 4 | 14 | |
| 5 | 7 | |
| 6 | 35 | |
| 7 | 36 | |
| 8 | 69 | |
| 9 | A Frustratingly Easy Approach for Entity and Relation Extractionbreakdown → | 266 |
| 10 | 172 | |
| 11 | 50 | |
| 12 | 8 | |
| 13 | Generating Regular Expressions from Natural Language Specifications: Are We There Yet? | 12 |
| 14 | 16 | |
| 15 | 57 | |
| 16 | 51 |
About Zexuan Zhong
Zexuan Zhong is a scholar working on Health Informatics, Artificial Intelligence and Software, having authored 16 papers that have together received 871 indexed citations. Recurring topics across this work include Topic Modeling (13 papers), Natural Language Processing Techniques (10 papers) and Multimodal Machine Learning Applications (3 papers). The work is most often cited by research in Artificial Intelligence (698 citations), Computer Vision and Pattern Recognition (179 citations) and Health Informatics (9 citations). Zexuan Zhong has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Danqi Chen, Dan Friedman, Mengzhou Xia, Tianyu Gao, Jinhyuk Lee, Yunfei Ma, Unsoo Ha, Fadel Adib, Zaiqing Nie and Mu Yao Guo. Their work appears in journals such as 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 and National Conference on Artificial Intelligence.
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.