Kam‐Fai Wong
- Artificial Intelligence top 0.2%
- Sociology and Political Science top 0.5%
- Information Systems top 0.2%
- Computer Vision and Pattern Recognition top 1%
- Statistical and Nonlinear Physics top 0.5%
- Topics
- Topic Modeling (115 papers)Natural Language Processing Techniques (60 papers)Advanced Text Analysis Techniques (44 papers)
- Journals
- GastroenterologyIEEE Transactions on Pattern Analysis and Machine IntelligenceIEEE Transactions on Automatic Control
- Partner nations
- Hong KongChinaUnited States
In The Last Decade
Kam‐Fai Wong
275 papers receiving 6.2k citations
Hit Papers
Peers
Comparison fields: 5 of 161
- Artificial Intelligence 3.6k
- Sociology and Political Science 2.3k
- Information Systems 2.1k
- Computer Vision and Pattern Recognition 1.1k
- Statistical and Nonlinear Physics 1.1k
Countries citing papers authored by Kam‐Fai Wong
This map shows the geographic impact of Kam‐Fai Wong'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 Kam‐Fai Wong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kam‐Fai Wong more than expected).
Fields of papers citing papers by Kam‐Fai Wong
This network shows the impact of papers produced by Kam‐Fai Wong. 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 Kam‐Fai Wong. The network helps show where Kam‐Fai Wong may publish in the future.
Co-authorship network of co-authors of Kam‐Fai Wong
This figure shows the co-authorship network connecting the top 25 collaborators of Kam‐Fai Wong. A scholar is included among the top collaborators of Kam‐Fai Wong 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 Kam‐Fai Wong. Kam‐Fai Wong 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 | 3 | |
| 3 | 3 | |
| 4 | 1 | |
| 5 | 9 | |
| 6 | 2 | |
| 7 | 3 | |
| 8 | 6 | |
| 9 | 3 | |
| 10 | 27 | |
| 11 | 11 | |
| 12 | 96 | |
| 13 | ACE: Automatic Colloquialism, Typographical and Orthographic Errors Detection for Chinese Language | 3 |
| 14 | 2 | |
| 15 | Quantising Opinions for Political Tweets Analysis | 18 |
| 16 | Cross-Lingual Identification of Ambiguous Discourse Connectives for Resource-Poor Language | 9 |
| 17 | Query Weighting for Ranking Model Adaptation | 7 |
| 18 | Design issues in a Chinese financial information extraction system | 0 |
| 19 | EDS: an advanced parallel database server | 0 |
| 20 | A Superimposed Codeword Indexing Scheme for Handling Sets in Prolog Databases | 1 |
About Kam‐Fai Wong
Kam‐Fai Wong is a scholar working on Artificial Intelligence, Information Systems and Signal Processing, having authored 299 papers that have together received 6.6k indexed citations. Recurring topics across this work include Topic Modeling (115 papers), Natural Language Processing Techniques (60 papers) and Advanced Text Analysis Techniques (44 papers). The work is most often cited by research in Artificial Intelligence (3.6k citations), Information Systems (2.1k citations) and Statistical and Nonlinear Physics (1.1k citations). Kam‐Fai Wong has collaborated with scholars based in Hong Kong, China and United States. Frequent co-authors include Wei Gao, Jing Ma, Friedrich M. Wahl, R. G. Casey, Robert W. P. Luk, K. L. Kwok, Jack Wu, Zhongyu Wei, Wenjie Li and E. Polak. Their work appears in journals such as Gastroenterology, IEEE Transactions on Pattern Analysis and Machine Intelligence and IEEE Transactions on Automatic Control.
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.