Itsumi Saito
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition
- Information Systems
- Molecular Biology
- Management Science and Operations Research
- Co-authors
- Hisako AsanoKyosuke NishidaJunji TomitaYoshihiro MatsuoKosuke NishidaRyo MasumuraRyota TanakaJun Suzuki
- Topics
- Topic Modeling (10 papers)Natural Language Processing Techniques (9 papers)Multimodal Machine Learning Applications (4 papers)
- Journals
- Language Resources and EvaluationNTT technical reviewarXiv (Cornell University)
- Partner nations
- Japan
In The Last Decade
Itsumi Saito
12 papers receiving 89 citations
Peers
Comparison fields: 5 of 20
- Artificial Intelligence 93
- Computer Vision and Pattern Recognition 27
- Information Systems 8
- Molecular Biology 4
- Management Science and Operations Research 4
Countries citing papers authored by Itsumi Saito
This map shows the geographic impact of Itsumi Saito'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 Itsumi Saito with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Itsumi Saito more than expected).
Fields of papers citing papers by Itsumi Saito
This network shows the impact of papers produced by Itsumi Saito. 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 Itsumi Saito. The network helps show where Itsumi Saito may publish in the future.
Co-authorship network of co-authors of Itsumi Saito
This figure shows the co-authorship network connecting the top 25 collaborators of Itsumi Saito. A scholar is included among the top collaborators of Itsumi Saito 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 Itsumi Saito. Itsumi Saito is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 9 | |
| 2 | 2 | |
| 3 | Unsupervised Domain Adaptation of Language Models for Reading Comprehension | 4 |
| 4 | How do Masked Language Models perform when the input sequence length changes | 1 |
| 5 | 2 | |
| 6 | 24 | |
| 7 | 3 | |
| 8 | 19 | |
| 9 | 8 | |
| 10 | Improving Neural Text Normalization with Data Augmentation at Character- and Morphological Levels | 12 |
| 11 | Automatically Extracting Variant-Normalization Pairs for Japanese Text Normalization | 2 |
| 12 | 2 | |
| 13 | Morphological Analysis for Japanese Noisy Text based on Character-level and Word-level Normalization | 14 |
| 14 | 0 |
About Itsumi Saito
Itsumi Saito is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Management Information Systems, having authored 14 papers that have together received 102 indexed citations. Recurring topics across this work include Topic Modeling (10 papers), Natural Language Processing Techniques (9 papers) and Multimodal Machine Learning Applications (4 papers). The work is most often cited by research in Artificial Intelligence (93 citations), Computer Vision and Pattern Recognition (27 citations) and Human Factors and Ergonomics (1 citation). Itsumi Saito has collaborated with scholars based in Japan. Frequent co-authors include Hisako Asano, Kyosuke Nishida, Junji Tomita, Yoshihiro Matsuo, Kosuke Nishida, Ryo Masumura, Ryota Tanaka, Jun Suzuki, Yūji Matsumoto and Atsushi Otsuka. Their work appears in journals such as Language Resources and Evaluation, NTT technical review and arXiv (Cornell University).
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.