Fei Cheng
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Experimental and Cognitive Psychology
- Management Science and Operations Research
- Signal Processing
- Co-authors
- Yusuke MiyaoJiangsheng YuSadao KurohashiIchiro KobayashiYūji MatsumotoKevin DuhMasayuki AsaharaQianying Liu
- Topics
- Topic Modeling (14 papers)Natural Language Processing Techniques (13 papers)Advanced Text Analysis Techniques (3 papers)
- Journals
- HeliyonLanguage Resources and EvaluationIEEE/ACM Transactions on Audio Speech and Language Processing
- Partner nations
- JapanChinaUnited States
In The Last Decade
Fei Cheng
21 papers receiving 202 citations
Peers
Comparison fields: 5 of 46
- Artificial Intelligence 151
- Computer Vision and Pattern Recognition 69
- Experimental and Cognitive Psychology 35
- Management Science and Operations Research 15
- Signal Processing 15
Countries citing papers authored by Fei Cheng
This map shows the geographic impact of Fei Cheng'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 Fei Cheng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fei Cheng more than expected).
Fields of papers citing papers by Fei Cheng
This network shows the impact of papers produced by Fei Cheng. 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 Fei Cheng. The network helps show where Fei Cheng may publish in the future.
Co-authorship network of co-authors of Fei Cheng
This figure shows the co-authorship network connecting the top 25 collaborators of Fei Cheng. A scholar is included among the top collaborators of Fei Cheng 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 Fei Cheng. Fei Cheng 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 | 7 | |
| 3 | 1 | |
| 4 | 6 | |
| 5 | 3 | |
| 6 | 1 | |
| 7 | 10 | |
| 8 | Towards a Versatile Medical-Annotation Guideline Feasible Without Heavy Medical Knowledge: Starting From Critical Lung Diseases | 4 |
| 9 | 8 | |
| 10 | Dependency Enhanced Contextual Representations for Japanese Temporal Relation Classification | 0 |
| 11 | 4 | |
| 12 | 1 | |
| 13 | Automatic Error Correction on Japanese Functional Expressions Using Character-based Neural Machine Translation | 2 |
| 14 | 2 | |
| 15 | 71 | |
| 16 | 4 | |
| 17 | Parsing Chinese Synthetic Words with a Character-based Dependency Model | 2 |
| 18 | 76 | |
| 19 | 2 | |
| 20 | 5 |
About Fei Cheng
Fei Cheng is a scholar working on Health Informatics, Artificial Intelligence and General Social Sciences, having authored 23 papers that have together received 217 indexed citations. Recurring topics across this work include Topic Modeling (14 papers), Natural Language Processing Techniques (13 papers) and Advanced Text Analysis Techniques (3 papers). The work is most often cited by research in Artificial Intelligence (151 citations), Computer Vision and Pattern Recognition (69 citations) and Health Informatics (4 citations). Fei Cheng has collaborated with scholars based in Japan, China and United States. Frequent co-authors include Yusuke Miyao, Jiangsheng Yu, Sadao Kurohashi, Ichiro Kobayashi, Yūji Matsumoto, Kevin Duh, Masayuki Asahara, Qianying Liu, Daisuke Kawahara and Sujian Li. Their work appears in journals such as Heliyon, Language Resources and Evaluation and IEEE/ACM Transactions on Audio Speech and Language Processing.
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.