Dingkun Long
Impact in
- Artificial Intelligence top 10%
- Text and Document Classification Technologies
- Topic Modeling
- Natural Language Processing Techniques
- Sentiment Analysis and Opinion Mining
- Advanced Text Analysis Techniques
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- Multimodal Machine Learning Applications
Papers in
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- Topic Modeling 9
- Natural Language Processing Techniques 6
- Domain Adaptation and Few-Shot Learning 2
- Sentiment Analysis and Opinion Mining 2
- Advanced Text Analysis Techniques 2
- Text and Document Classification Technologies 2
- Neural Networks and Applications 2
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- Multimodal Machine Learning Applications 5
- Co-authors
- Pengjun Xie (9 shared papers)Guangwei Xu (4 shared papers)Jie Zhou (1 shared paper)Haoyu Zhang (1 shared paper)Chunping Ma (1 shared paper)Ning Ding (1 shared paper)Gongshen Liu (1 shared paper)Yongyi Mao (2 shared papers)
- Journals
- Neurocomputing (1 paper)IEEE Access (1 paper)Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (1 paper)Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (1 paper)
- Partner nations
- ChinaUnited StatesCanada
In The Last Decade
Dingkun Long
11 papers receiving 178 citations
Peers
Comparison fields: 5 of 42
- Artificial Intelligence 142
- Computer Vision and Pattern Recognition 25
- Information Systems 27
- Signal Processing 9
- Family Practice 1
Countries citing papers authored by Dingkun Long
This map shows the geographic impact of Dingkun Long'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 Dingkun Long with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dingkun Long more than expected).
Fields of papers citing papers by Dingkun Long
This network shows the impact of papers produced by Dingkun Long. 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 Dingkun Long. The network helps show where Dingkun Long may publish in the future.
Co-authors
The 25 scholars most cited alongside Dingkun Long, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 116 | |
| 2 | 2020 | 20 | |
| 3 | 2024 | 15 | |
| 4 | 2019 | 8 | |
| 5 | 2022 | 7 | |
| 6 | 2022 | 5 | |
| 7 | 2018 | 5 | |
| 8 | 2024 | 3 | |
| 9 | 2021 | 2 | |
| 10 | 2025 | 1 | |
| 11 | 2023 | 1 | |
| 12 | 2025 | 0 | |
| 13 | 2024 | 0 |
About Dingkun Long
Dingkun Long is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Statistical and Nonlinear Physics and Signal Processing, having authored 13 papers that have together received 183 indexed citations. Recurring topics across this work include Topic Modeling (9 papers), Natural Language Processing Techniques (6 papers), Multimodal Machine Learning Applications (5 papers), Domain Adaptation and Few-Shot Learning (2 papers), Sentiment Analysis and Opinion Mining (2 papers), Advanced Text Analysis Techniques (2 papers), Text and Document Classification Technologies (2 papers) and Neural Networks and Applications (2 papers). The work is most often cited by research in Artificial Intelligence (142 citations), Computer Vision and Pattern Recognition (25 citations), Information Systems (27 citations), Signal Processing (9 citations) and Family Practice (1 citation). Dingkun Long has collaborated with scholars based in China, United States and Canada. Frequent co-authors include Pengjun Xie, Guangwei Xu, Jie Zhou, Haoyu Zhang, Chunping Ma, Ning Ding, Gongshen Liu, Yongyi Mao, Richong Zhang and Ji Wang. Their work appears in journals such as Neurocomputing, IEEE Access, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing and Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval.
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.