Chung-Hong Lee
- Artificial Intelligence top 5%
- Information Systems top 5%
- Electrical and Electronic Engineering
- Computer Vision and Pattern Recognition top 10%
- Automotive Engineering top 10%
- Co-authors
- Hsin-Chang YangChih‐Hung WuShih‐Hao WangWen-Kai HsuYuchen WeiYenming J. ChenDing-Wen ChenYacine Terriche
- Topics
- Text and Document Classification Technologies (29 papers)Advanced Text Analysis Techniques (16 papers)Web Data Mining and Analysis (16 papers)
In The Last Decade
Chung-Hong Lee
83 papers receiving 753 citations
Peers
Comparison fields: 5 of 99
- Artificial Intelligence 393
- Information Systems 212
- Electrical and Electronic Engineering 127
- Computer Vision and Pattern Recognition 108
- Automotive Engineering 107
Countries citing papers authored by Chung-Hong Lee
This map shows the geographic impact of Chung-Hong Lee'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 Chung-Hong Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chung-Hong Lee more than expected).
Fields of papers citing papers by Chung-Hong Lee
This network shows the impact of papers produced by Chung-Hong Lee. 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 Chung-Hong Lee. The network helps show where Chung-Hong Lee may publish in the future.
Co-authorship network of co-authors of Chung-Hong Lee
This figure shows the co-authorship network connecting the top 25 collaborators of Chung-Hong Lee. A scholar is included among the top collaborators of Chung-Hong Lee 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 Chung-Hong Lee. Chung-Hong Lee 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 | 2 | |
| 3 | 1 | |
| 4 | 6 | |
| 5 | 16 | |
| 6 | 3 | |
| 7 | 1 | |
| 8 | 0 | |
| 9 | 25 | |
| 10 | 18 | |
| 11 | Identifying spam tags by mining tag semantics | 1 |
| 12 | 11 | |
| 13 | 5 | |
| 14 | Development of a Multi-Classifier Approach for Multilingual Text Categorization. | 1 |
| 15 | A New Measure of Text Relatedness Using a Novel Classifier-based Vector Approach. | 1 |
| 16 | 1 | |
| 17 | 12 | |
| 18 | 23 | |
| 19 | 7 | |
| 20 | 2 |
About Chung-Hong Lee
Chung-Hong Lee is a scholar working on Artificial Intelligence, Information Systems and Statistical and Nonlinear Physics, having authored 87 papers that have together received 828 indexed citations. Recurring topics across this work include Text and Document Classification Technologies (29 papers), Advanced Text Analysis Techniques (16 papers) and Web Data Mining and Analysis (16 papers). The work is most often cited by research in Artificial Intelligence (393 citations), Information Systems (212 citations) and Automotive Engineering (107 citations). Chung-Hong Lee has collaborated with scholars based in Taiwan, Australia and Denmark. Frequent co-authors include Hsin-Chang Yang, Chih‐Hung Wu, Shih‐Hao Wang, Wen-Kai Hsu, Yuchen Wei, Yenming J. Chen, Ding-Wen Chen, Chih‐Hung Wu, Yacine Terriche and Muhammad Sadiq. Their work appears in journals such as Expert Systems with Applications, IEEE Access and Journal of Medical Internet Research.
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.