Tomoko Izumi
- Computer Networks and Communications top 10%
- Cognitive Neuroscience
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Human-Computer Interaction top 10%
- Co-authors
- Kenji ImamuraTohru IfukubeFukuhito OoshitaMakoto TakahashiShohei ShimizuShuichi InoManami SatoTaisuke Izumi
- Topics
- Natural Language Processing Techniques (9 papers)Distributed systems and fault tolerance (9 papers)Topic Modeling (7 papers)
In The Last Decade
Tomoko Izumi
44 papers receiving 288 citations
Peers
Comparison fields: 5 of 52
- Computer Networks and Communications 91
- Cognitive Neuroscience 81
- Artificial Intelligence 70
- Computer Vision and Pattern Recognition 49
- Human-Computer Interaction 41
Countries citing papers authored by Tomoko Izumi
This map shows the geographic impact of Tomoko Izumi'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 Tomoko Izumi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tomoko Izumi more than expected).
Fields of papers citing papers by Tomoko Izumi
This network shows the impact of papers produced by Tomoko Izumi. 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 Tomoko Izumi. The network helps show where Tomoko Izumi may publish in the future.
Co-authorship network of co-authors of Tomoko Izumi
This figure shows the co-authorship network connecting the top 25 collaborators of Tomoko Izumi. A scholar is included among the top collaborators of Tomoko Izumi 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 Tomoko Izumi. Tomoko Izumi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 2 | |
| 3 | 6 | |
| 4 | 5 | |
| 5 | 2 | |
| 6 | Predicate-Argument Structure Analysis with Zero-Anaphora Resolution for Dialogue Systems | 6 |
| 7 | Constructing a Corpus of Japanese Predicate Phrases for Synonym/Antonym Relations | 1 |
| 8 | 9 | |
| 9 | 6 | |
| 10 | 2 | |
| 11 | 4 | |
| 12 | 0 | |
| 13 | 2 | |
| 14 | Standardizing Complex Functional Expressions in Japanese Predicates: Applying Theoretically-Based Paraphrasing Rules | 3 |
| 15 | 0 | |
| 16 | 5 | |
| 17 | 10 | |
| 18 | 5 | |
| 19 | 1 | |
| 20 | 3 |
About Tomoko Izumi
Tomoko Izumi is a scholar working on Human-Computer Interaction, Computer Networks and Communications and Signal Processing, having authored 52 papers that have together received 304 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (9 papers), Distributed systems and fault tolerance (9 papers) and Topic Modeling (7 papers). The work is most often cited by research in Human-Computer Interaction (41 citations), Cognitive Neuroscience (81 citations) and Computer Networks and Communications (91 citations). Tomoko Izumi has collaborated with scholars based in Japan, Canada and India. Frequent co-authors include Kenji Imamura, Tohru Ifukube, Fukuhito Ooshita, Makoto Takahashi, Shohei Shimizu, Shuichi Ino, Manami Sato, Taisuke Izumi, Toshimitsu Masuzawa and Hiroshi Tsutsui. Their work appears in journals such as IEEE Transactions on Parallel and Distributed Systems, Theoretical Computer Science and Ad Hoc Networks.
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.