Min‐Yen Kan
- Artificial Intelligence top 0.1%
- Information Systems top 0.1%
- Computer Vision and Pattern Recognition top 1%
- Management Science and Operations Research top 1%
- Molecular Biology
- Co-authors
- Xiangnan HeTat‐Seng ChuaKazunari SugiyamaZiheng LinHanwang ZhangHwee Tou NgSu Nam KimTao Chen
- Topics
- Topic Modeling (120 papers)Natural Language Processing Techniques (95 papers)Advanced Text Analysis Techniques (40 papers)
- Partner nations
- SingaporeUnited StatesChina
In The Last Decade
Min‐Yen Kan
209 papers receiving 5.6k citations
Hit Papers
Peers
Comparison fields: 5 of 138
- Artificial Intelligence 4.6k
- Information Systems 2.5k
- Computer Vision and Pattern Recognition 900
- Management Science and Operations Research 495
- Molecular Biology 407
Countries citing papers authored by Min‐Yen Kan
This map shows the geographic impact of Min‐Yen Kan'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 Min‐Yen Kan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Min‐Yen Kan more than expected).
Fields of papers citing papers by Min‐Yen Kan
This network shows the impact of papers produced by Min‐Yen Kan. 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 Min‐Yen Kan. The network helps show where Min‐Yen Kan may publish in the future.
Co-authorship network of co-authors of Min‐Yen Kan
This figure shows the co-authorship network connecting the top 25 collaborators of Min‐Yen Kan. A scholar is included among the top collaborators of Min‐Yen Kan 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 Min‐Yen Kan. Min‐Yen Kan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 2 | |
| 5 | 3 | |
| 6 | 7 | |
| 7 | 0 | |
| 8 | 2 | |
| 9 | 3 | |
| 10 | 1 | |
| 11 | 0 | |
| 12 | 7 | |
| 13 | 7 | |
| 14 | 8 | |
| 15 | 3 | |
| 16 | 22 | |
| 17 | Chinese Informal Word Normalization: an Experimental Study | 9 |
| 18 | Improved Temporal Relation Classification using Dependency Parses and Selective Crowdsourced Annotations | 7 |
| 19 | An empirical study of students' perceptions on e-learning systems | 5 |
| 20 | National University of Singapore at the TREC 13 Question Answering Main Task. | 26 |
About Min‐Yen Kan
Min‐Yen Kan is a scholar working on Artificial Intelligence, Information Systems and Issues, ethics and legal aspects, having authored 226 papers that have together received 6.0k indexed citations. Recurring topics across this work include Topic Modeling (120 papers), Natural Language Processing Techniques (95 papers) and Advanced Text Analysis Techniques (40 papers). The work is most often cited by research in Artificial Intelligence (4.6k citations), Information Systems (2.5k citations) and Computer Vision and Pattern Recognition (900 citations). Min‐Yen Kan has collaborated with scholars based in Singapore, United States and China. Frequent co-authors include Xiangnan He, Tat‐Seng Chua, Kazunari Sugiyama, Ziheng Lin, Hanwang Zhang, Hwee Tou Ng, Su Nam Kim, Tao Chen, Judith L. Klavans and Hang Cui. Their work appears in journals such as Nucleic Acids Research, Scientific Reports and Communications of the ACM.
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.