Qun Liu
- Artificial Intelligence top 0.2%
- Computer Vision and Pattern Recognition top 1%
- Information Systems top 2%
- Molecular Biology
- Signal Processing top 5%
- Co-authors
- Lifeng ShangXin JiangShouxun LinYichun YinXiao Dong ChenLinlin LiXiaoqi JiaoFang Wang
- Topics
- Topic Modeling (178 papers)Natural Language Processing Techniques (172 papers)Multimodal Machine Learning Applications (47 papers)
- Journals
- SHILAP Revista de lepidopterologíaJournal of Materials Chemistry AOptics Express
In The Last Decade
Qun Liu
219 papers receiving 3.6k citations
Hit Papers
Peers
Comparison fields: 5 of 124
- Artificial Intelligence 3.3k
- Computer Vision and Pattern Recognition 1.2k
- Information Systems 334
- Molecular Biology 202
- Signal Processing 128
Countries citing papers authored by Qun Liu
This map shows the geographic impact of Qun Liu'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 Qun Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Qun Liu more than expected).
Fields of papers citing papers by Qun Liu
This network shows the impact of papers produced by Qun Liu. 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 Qun Liu. The network helps show where Qun Liu may publish in the future.
Co-authorship network of co-authors of Qun Liu
This figure shows the co-authorship network connecting the top 25 collaborators of Qun Liu. A scholar is included among the top collaborators of Qun Liu 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 Qun Liu. Qun Liu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 3 | |
| 4 | 2 | |
| 5 | 10 | |
| 6 | 3 | |
| 7 | 1 | |
| 8 | 3 | |
| 9 | 3 | |
| 10 | 11 | |
| 11 | 2 | |
| 12 | 2 | |
| 13 | 10 | |
| 14 | 20 | |
| 15 | 8 | |
| 16 | Improving character-based decoding using target-side morphological\ninformation for neural machine translation | 9 |
| 17 | Topic-Informed Neural Machine Translation | 17 |
| 18 | Modeling lexical cohesion for document-level machine translation | 17 |
| 19 | Left-to-Right Tree-to-String Decoding with Prediction | 5 |
| 20 | The ICT 's Patent MT System Description for NTCIR-9 | 1 |
About Qun Liu
Qun Liu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Mathematics, having authored 230 papers that have together received 3.9k indexed citations. Recurring topics across this work include Topic Modeling (178 papers), Natural Language Processing Techniques (172 papers) and Multimodal Machine Learning Applications (47 papers). The work is most often cited by research in Artificial Intelligence (3.3k citations), Computer Vision and Pattern Recognition (1.2k citations) and Information Systems (334 citations). Qun Liu has collaborated with scholars based in China, Ireland and Sweden. Frequent co-authors include Lifeng Shang, Xin Jiang, Shouxun Lin, Yichun Yin, Xiao Dong Chen, Linlin Li, Xiaoqi Jiao, Fang Wang, Yang Liu and Yajuan Lü. Their work appears in journals such as SHILAP Revista de lepidopterología, Journal of Materials Chemistry A and Optics Express.
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.