Tetsuya Sakai
- Information Systems top 0.2%
- Artificial Intelligence top 0.5%
- Polymers and Plastics top 5%
- Computer Vision and Pattern Recognition top 5%
- Electrical and Electronic Engineering top 10%
- Co-authors
- Ruihua SongZhicheng DouNoriko KandoMasatoshi KubouchiFujio MasudaOsamu FujiwaraMakoto P. KatoTakashi Inoue
- Topics
- Topic Modeling (96 papers)Information Retrieval and Search Behavior (96 papers)Natural Language Processing Techniques (46 papers)
- Partner nations
- JapanUnited StatesChina
In The Last Decade
Tetsuya Sakai
370 papers receiving 4.0k citations
Hit Papers
Peers
Comparison fields: 5 of 163
- Information Systems 1.7k
- Artificial Intelligence 1.6k
- Polymers and Plastics 371
- Computer Vision and Pattern Recognition 333
- Electrical and Electronic Engineering 322
Countries citing papers authored by Tetsuya Sakai
This map shows the geographic impact of Tetsuya Sakai'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 Tetsuya Sakai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tetsuya Sakai more than expected).
Fields of papers citing papers by Tetsuya Sakai
This network shows the impact of papers produced by Tetsuya Sakai. 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 Tetsuya Sakai. The network helps show where Tetsuya Sakai may publish in the future.
Co-authorship network of co-authors of Tetsuya Sakai
This figure shows the co-authorship network connecting the top 25 collaborators of Tetsuya Sakai. A scholar is included among the top collaborators of Tetsuya Sakai 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 Tetsuya Sakai. Tetsuya Sakai 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 | 0 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 0 | |
| 7 | 4 | |
| 8 | 9 | |
| 9 | 0 | |
| 10 | 1 | |
| 11 | 1 | |
| 12 | Overview of NTCIR-9 | 15 |
| 13 | NTCIREVAL: A Generic Toolkit for Information Access Evaluation | 11 |
| 14 | Overview of the NTCIR-9 INTENT Task | 46 |
| 15 | 4 | |
| 16 | 13 | |
| 17 | 9 | |
| 18 | 19 | |
| 19 | 7 | |
| 20 | 13 |
About Tetsuya Sakai
Tetsuya Sakai is a scholar working on Information Systems, Artificial Intelligence and Earth-Surface Processes, having authored 410 papers that have together received 4.3k indexed citations. Recurring topics across this work include Topic Modeling (96 papers), Information Retrieval and Search Behavior (96 papers) and Natural Language Processing Techniques (46 papers). The work is most often cited by research in Information Systems (1.7k citations), Artificial Intelligence (1.6k citations) and Computer Science Applications (204 citations). Tetsuya Sakai has collaborated with scholars based in Japan, United States and China. Frequent co-authors include Ruihua Song, Zhicheng Dou, Noriko Kando, Masatoshi Kubouchi, Fujio Masuda, Osamu Fujiwara, Makoto P. Kato, Takashi Inoue, Yuji Fujita and Susumu Umemoto. Their work appears in journals such as Nature, Proceedings of the National Academy of Sciences and Journal of Clinical Oncology.
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.