Yan Song
- Artificial Intelligence top 0.5%
- Computer Vision and Pattern Recognition top 2%
- Molecular Biology
- Radiology, Nuclear Medicine and Imaging top 10%
- Information Systems top 5%
- Topics
- Topic Modeling (55 papers)Natural Language Processing Techniques (50 papers)Multimodal Machine Learning Applications (11 papers)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Yan Song
70 papers receiving 1.8k citations
Peers
Comparison fields: 5 of 102
- Artificial Intelligence 1.5k
- Computer Vision and Pattern Recognition 398
- Molecular Biology 190
- Radiology, Nuclear Medicine and Imaging 153
- Information Systems 142
Countries citing papers authored by Yan Song
This map shows the geographic impact of Yan Song'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 Yan Song with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yan Song more than expected).
Fields of papers citing papers by Yan Song
This network shows the impact of papers produced by Yan Song. 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 Yan Song. The network helps show where Yan Song may publish in the future.
Co-authorship network of co-authors of Yan Song
This figure shows the co-authorship network connecting the top 25 collaborators of Yan Song. A scholar is included among the top collaborators of Yan Song 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 Yan Song. Yan Song 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 | 5 | |
| 4 | 0 | |
| 5 | 7 | |
| 6 | 2 | |
| 7 | 0 | |
| 8 | 36 | |
| 9 | 15 | |
| 10 | 56 | |
| 11 | 96 | |
| 12 | 24 | |
| 13 | 18 | |
| 14 | 29 | |
| 15 | Constructing a Chinese Medical Conversation Corpus Annotated with Conversational Structures and Actions. | 6 |
| 16 | Non-Monotonic Sentence Alignment via Semisupervised Learning | 4 |
| 17 | How Large a Corpus Do We Need: Statistical Method Versus Rule-based Method | 8 |
| 18 | An Empirical Study on Development Set Selection Strategy for Machine Translation Learning | 5 |
| 19 | Chinese Word Segmentation Based on an Approach of Maximum Entropy Modeling | 3 |
| 20 | Modification of commercial activated carbon through gasification by impregnated metal salts to develop mesoporous structures | 16 |
About Yan Song
Yan Song is a scholar working on Artificial Intelligence, Health Informatics and Computer Vision and Pattern Recognition, having authored 80 papers that have together received 1.8k indexed citations. Recurring topics across this work include Topic Modeling (55 papers), Natural Language Processing Techniques (50 papers) and Multimodal Machine Learning Applications (11 papers). The work is most often cited by research in Artificial Intelligence (1.5k citations), Health Informatics (47 citations) and Computer Vision and Pattern Recognition (398 citations). Yan Song has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Yuanhe Tian, Xiang Wan, Zhihong Chen, Guimin Chen, Tsung‐Hui Chang, Fei Xia, Fei Xia, Tong Zhang, Shizhe Diao and Jing Li. Their work appears in journals such as IEEE Transactions on Medical Imaging, BMC Bioinformatics and Applied Catalysis A General.
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.