Tatsuki Kuribayashi
- Artificial Intelligence top 5%
- Natural Language Processing Techniques 20
- Topic Modeling 19
- Speech and dialogue systems 5
- Text Readability and Simplification 3
- Multi-Agent Systems and Negotiation 2
- Explainable Artificial Intelligence (XAI) 2
- Neural Networks and Applications 1
-
- Software Engineering Research 4
- Co-authors
- Kentaro InuiGoro KobayashiJun SuzukiYohei OsekiHiroki OuchiNaoya InoueMasayuki AsaharaSosuke Kobayashi
- Journals
- Transactions of the Association for Computational Linguistics (1 paper)Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (1 paper)Journal of Natural Language Processing (2 papers)
- Partner nations
- JapanUnited Arab EmiratesUnited States
In The Last Decade
Tatsuki Kuribayashi
19 papers receiving 238 citations
Peers
Comparison fields: 5 of 48
- Artificial Intelligence 214
- Health Informatics 3
- Computer Vision and Pattern Recognition 36
- Information Systems 33
- Cognitive Neuroscience 25
Countries citing papers authored by Tatsuki Kuribayashi
This map shows the geographic impact of Tatsuki Kuribayashi'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 Tatsuki Kuribayashi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tatsuki Kuribayashi more than expected).
Fields of papers citing papers by Tatsuki Kuribayashi
This network shows the impact of papers produced by Tatsuki Kuribayashi. 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 Tatsuki Kuribayashi. The network helps show where Tatsuki Kuribayashi may publish in the future.
Co-authorship network
The 17 scholars most cited alongside Tatsuki Kuribayashi, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 3 | |
| 2 | 2024 | 0 | |
| 3 | 2024 | 0 | |
| 4 | 2024 | 0 | |
| 5 | 2023 | 3 | |
| 6 | 2023 | 1 | |
| 7 | 2023 | 9 | |
| 8 | 2023 | 1 | |
| 9 | 2023 | 0 | |
| 10 | 2023 | 2 | |
| 11 | 2022 | 16 | |
| 12 | 2021 | 32 | |
| 13 | 2021 | 12 | |
| 14 | 2020 | 79 | |
| 15 | 2020 | 5 | |
| 16 | 2020 | 0 | |
| 17 | 2020 | 9 | |
| 18 | 2020 | 4 | |
| 19 | 2019 | 4 | |
| 20 | 2019 | 8 |
About Tatsuki Kuribayashi
Tatsuki Kuribayashi is a scholar working on Artificial Intelligence, Information Systems and Literature and Literary Theory, having authored 24 papers that have together received 251 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (20 papers), Topic Modeling (19 papers), Speech and dialogue systems (5 papers), Software Engineering Research (4 papers), Text Readability and Simplification (3 papers), Multi-Agent Systems and Negotiation (2 papers), Explainable Artificial Intelligence (XAI) (2 papers) and Neural Networks and Applications (1 paper). The work is most often cited by research in Artificial Intelligence (214 citations), Health Informatics (3 citations) and Computer Vision and Pattern Recognition (36 citations). Tatsuki Kuribayashi has collaborated with scholars based in Japan, United Arab Emirates and United States. Frequent co-authors include Kentaro Inui, Goro Kobayashi, Jun Suzuki, Yohei Oseki, Hiroki Ouchi, Naoya Inoue, Masayuki Asahara, Sosuke Kobayashi, Masato Hagiwara and Keisuke Sakaguchi. Their work appears in journals such as Transactions of the Association for Computational Linguistics, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing and Journal of Natural 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.