Yee Fan Tan
- Artificial Intelligence top 5%
- Information Systems top 5%
- Management Science and Operations Research top 5%
- Molecular Biology
- Computer Vision and Pattern Recognition
- Co-authors
- Min‐Yen KanDongwon LeeRobert DaleSteven BirdBryan R. GibsonDragomir RadevBonnie J. DorrHang Cui
- Topics
- Data Quality and Management (5 papers)Natural Language Processing Techniques (5 papers)Topic Modeling (4 papers)
In The Last Decade
Yee Fan Tan
13 papers receiving 327 citations
Peers
Comparison fields: 5 of 54
- Artificial Intelligence 305
- Information Systems 116
- Management Science and Operations Research 97
- Molecular Biology 54
- Computer Vision and Pattern Recognition 26
Countries citing papers authored by Yee Fan Tan
This map shows the geographic impact of Yee Fan Tan'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 Yee Fan Tan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yee Fan Tan more than expected).
Fields of papers citing papers by Yee Fan Tan
This network shows the impact of papers produced by Yee Fan Tan. 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 Yee Fan Tan. The network helps show where Yee Fan Tan may publish in the future.
Co-authorship network of co-authors of Yee Fan Tan
This figure shows the co-authorship network connecting the top 25 collaborators of Yee Fan Tan. A scholar is included among the top collaborators of Yee Fan Tan 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 Yee Fan Tan. Yee Fan Tan 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 | 0 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 2 | |
| 6 | 9 | |
| 7 | 4 | |
| 8 | 7 | |
| 9 | 23 | |
| 10 | 0 | |
| 11 | The ACL Anthology Reference Corpus: A Reference Dataset for Bibliographic Research in Computational Linguistics | 149 |
| 12 | Efficient Web-Based Linkage of Short to Long Forms | 3 |
| 13 | 9 | |
| 14 | 4 | |
| 15 | 43 | |
| 16 | 61 | |
| 17 | Extending corpus-based identification of light verb constructions using a supervised learning framework | 20 |
| 18 | 38 |
About Yee Fan Tan
Yee Fan Tan is a scholar working on Health Informatics, Management Science and Operations Research and Artificial Intelligence, having authored 18 papers that have together received 372 indexed citations. Recurring topics across this work include Data Quality and Management (5 papers), Natural Language Processing Techniques (5 papers) and Topic Modeling (4 papers). The work is most often cited by research in Artificial Intelligence (305 citations), Management Science and Operations Research (97 citations) and Information Systems (116 citations). Yee Fan Tan has collaborated with scholars based in Singapore, Malaysia and Australia. Frequent co-authors include Min‐Yen Kan, Dongwon Lee, Robert Dale, Steven Bird, Bryan R. Gibson, Dragomir Radev, Bonnie J. Dorr, Hang Cui, Yan Su and Ergin Elmacioglu. Their work appears in journals such as Communications of the ACM, Applied Sciences and Language Resources and Evaluation.
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.