Li Dong
- Artificial Intelligence top 0.5%
- Computer Vision and Pattern Recognition top 2%
- Information Systems top 2%
- Signal Processing top 5%
- Management Science and Operations Research top 10%
- Topics
- Topic Modeling (35 papers)Natural Language Processing Techniques (33 papers)Multimodal Machine Learning Applications (15 papers)
- Journals
- Information SciencesApplied Soft ComputingIEEE Transactions on Circuits and Systems for Video Technology
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Li Dong
56 papers receiving 2.7k citations
Hit Papers
Peers
Comparison fields: 5 of 117
- Artificial Intelligence 2.3k
- Computer Vision and Pattern Recognition 643
- Information Systems 366
- Signal Processing 130
- Management Science and Operations Research 79
Countries citing papers authored by Li Dong
This map shows the geographic impact of Li Dong'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 Li Dong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Li Dong more than expected).
Fields of papers citing papers by Li Dong
This network shows the impact of papers produced by Li Dong. 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 Li Dong. The network helps show where Li Dong may publish in the future.
Co-authorship network of co-authors of Li Dong
This figure shows the co-authorship network connecting the top 25 collaborators of Li Dong. A scholar is included among the top collaborators of Li Dong 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 Li Dong. Li Dong is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 0 | |
| 6 | 2 | |
| 7 | 3 | |
| 8 | 3 | |
| 9 | 1 | |
| 10 | 26 | |
| 11 | 2 | |
| 12 | 115 | |
| 13 | 143 | |
| 14 | 2 | |
| 15 | 90 | |
| 16 | 206 | |
| 17 | 115 | |
| 18 | Long Short-Term Memory-Networks for Machine Readingbreakdown → | 664 |
| 19 | Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classificationbreakdown → | 708 |
| 20 | Transformation from MapInfo file to SVG in WebGIS application | 2 |
About Li Dong
Li Dong is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems, having authored 63 papers that have together received 2.8k indexed citations. Recurring topics across this work include Topic Modeling (35 papers), Natural Language Processing Techniques (33 papers) and Multimodal Machine Learning Applications (15 papers). The work is most often cited by research in Artificial Intelligence (2.3k citations), Computer Vision and Pattern Recognition (643 citations) and Information Systems (366 citations). Li Dong has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Mirella Lapata, Furu Wei, Jianpeng Cheng, Ke Xu, Ming Zhou, Duyu Tang, Chuanqi Tan, Shaohan Huang, Yaru Hao and Zewen Chi. Their work appears in journals such as Information Sciences, Applied Soft Computing and IEEE Transactions on Circuits and Systems for Video Technology.
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.