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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- Web Data Mining and Analysis
- Spam and Phishing Detection
Papers in
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- Topic Modeling 8
- Natural Language Processing Techniques 5
- Advanced Text Analysis Techniques 2
- Domain Adaptation and Few-Shot Learning 2
- Sentiment Analysis and Opinion Mining 2
- Neural Networks and Applications 2
- Text and Document Classification Technologies 2
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- Multimodal Machine Learning Applications 4
- Co-authors
- Pengjun Xie (9 shared papers)Guangwei Xu (4 shared papers)Gongshen Liu (1 shared paper)Ning Ding (1 shared paper)Chunping Ma (1 shared paper)Haoyu Zhang (1 shared paper)Jie Zhou (1 shared paper)Richong Zhang (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 140 citations
Peers
Comparison fields: 5 of 41
- Artificial Intelligence 114
- Information Systems 24
- Computer Vision and Pattern Recognition 17
- Signal Processing 6
- 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 | 92 | |
| 2 | 2020 | 16 | |
| 3 | 2024 | 8 | |
| 4 | 2019 | 8 | |
| 5 | 2022 | 6 | |
| 6 | 2022 | 5 | |
| 7 | 2018 | 4 | |
| 8 | 2021 | 2 | |
| 9 | 2024 | 1 | |
| 10 | 2025 | 1 | |
| 11 | 2023 | 1 |
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 11 papers that have together received 144 indexed citations. Recurring topics across this work include Topic Modeling (8 papers), Natural Language Processing Techniques (5 papers), Multimodal Machine Learning Applications (4 papers), Advanced Text Analysis Techniques (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Sentiment Analysis and Opinion Mining (2 papers), Neural Networks and Applications (2 papers) and Text and Document Classification Technologies (2 papers). The work is most often cited by research in Artificial Intelligence (114 citations), Information Systems (24 citations), Computer Vision and Pattern Recognition (17 citations), Signal Processing (6 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, Gongshen Liu, Ning Ding, Chunping Ma, Haoyu Zhang, Jie Zhou, Richong Zhang, Yongyi Mao and Fei Huang. 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.