Ryuichi Takanobu
- Artificial Intelligence top 5%
- Topic Modeling 13
- Speech and dialogue systems 8
- Natural Language Processing Techniques 7
- Sentiment Analysis and Opinion Mining 3
- Multi-Agent Systems and Negotiation 2
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- Multimodal Machine Learning Applications 4
- Information Systems top 10%
- Service-Oriented Architecture and Web Services 2
- Recommender Systems and Techniques 2
- Co-authors
- Minlie HuangTianyang ZhangXiaoyan ZhuQi ZhuZheng ZhangJie ZhouHeng JiPeng Li
- Journals
- IEEE Transactions on Knowledge and Data Engineering (1 paper)IEEE/ACM Transactions on Audio Speech and Language Processing (1 paper)Science China Technological Sciences (1 paper)
- Partner nations
- ChinaUnited StatesSwitzerland
In The Last Decade
Ryuichi Takanobu
15 papers receiving 452 citations
Peers
Comparison fields: 5 of 55
- Artificial Intelligence 418
- Computer Vision and Pattern Recognition 90
- Information Systems 67
- Health Informatics 4
- Management Science and Operations Research 25
Countries citing papers authored by Ryuichi Takanobu
This map shows the geographic impact of Ryuichi Takanobu'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 Ryuichi Takanobu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ryuichi Takanobu more than expected).
Fields of papers citing papers by Ryuichi Takanobu
This network shows the impact of papers produced by Ryuichi Takanobu. 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 Ryuichi Takanobu. The network helps show where Ryuichi Takanobu may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Ryuichi Takanobu, 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 | 31 | |
| 2 | 2022 | 3 | |
| 3 | Multi-domain Task-oriented Dialog Challenge II at DSTC9 | 2021 | 3 |
| 4 | 2021 | 29 | |
| 5 | 2021 | 59 | |
| 6 | 2021 | 18 | |
| 7 | 2021 | 8 | |
| 8 | 2021 | 1 | |
| 9 | 2020 | 1 | |
| 10 | Results of the Multi-Domain Task-Completion Dialog Challenge | 2020 | 13 |
| 11 | 2020 | 12 | |
| 12 | 2020 | 28 | |
| 13 | 2020 | 11 | |
| 14 | 2020 | 97 | |
| 15 | 2019 | 130 | |
| 16 | 2018 | 25 |
About Ryuichi Takanobu
Ryuichi Takanobu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems, having authored 16 papers that have together received 469 indexed citations. Recurring topics across this work include Topic Modeling (13 papers), Speech and dialogue systems (8 papers), Natural Language Processing Techniques (7 papers), Multimodal Machine Learning Applications (4 papers), Sentiment Analysis and Opinion Mining (3 papers), Service-Oriented Architecture and Web Services (2 papers), Multi-Agent Systems and Negotiation (2 papers) and Recommender Systems and Techniques (2 papers). The work is most often cited by research in Artificial Intelligence (418 citations), Computer Vision and Pattern Recognition (90 citations) and Information Systems (67 citations). Ryuichi Takanobu has collaborated with scholars based in China, United States and Switzerland. Frequent co-authors include Minlie Huang, Tianyang Zhang, Xiaoyan Zhu, Qi Zhu, Minlie Huang, Zheng Zhang, Jie Zhou, Heng Ji, Peng Li and Yujia Qin. Their work appears in journals such as IEEE Transactions on Knowledge and Data Engineering, IEEE/ACM Transactions on Audio Speech and Language Processing and Science China Technological Sciences.
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.