Sho Takase
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition
- Information Systems
- Molecular Biology
- Electrical and Electronic Engineering
- Co-authors
- Naoaki OkazakiJun SuzukiMasaaki NagataTsutomu HiraoShun KiyonoKentaro InuiAtsushi KeyakiNaoya Inoue
- Topics
- Topic Modeling (27 papers)Natural Language Processing Techniques (25 papers)Advanced Text Analysis Techniques (7 papers)
- Journals
- Engineering Applications of Artificial IntelligenceLanguage Resources and EvaluationACM Transactions on Asian and Low-Resource Language Information Processing
- Partner nations
- JapanUnited States
In The Last Decade
Sho Takase
27 papers receiving 295 citations
Peers
Comparison fields: 5 of 39
- Artificial Intelligence 286
- Computer Vision and Pattern Recognition 52
- Information Systems 29
- Molecular Biology 12
- Electrical and Electronic Engineering 8
Countries citing papers authored by Sho Takase
This map shows the geographic impact of Sho Takase'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 Sho Takase with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sho Takase more than expected).
Fields of papers citing papers by Sho Takase
This network shows the impact of papers produced by Sho Takase. 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 Sho Takase. The network helps show where Sho Takase may publish in the future.
Co-authorship network of co-authors of Sho Takase
This figure shows the co-authorship network connecting the top 25 collaborators of Sho Takase. A scholar is included among the top collaborators of Sho Takase 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 Sho Takase. Sho Takase is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 15 | |
| 2 | 0 | |
| 3 | 4 | |
| 4 | 4 | |
| 5 | 0 | |
| 6 | 0 | |
| 7 | 3 | |
| 8 | 3 | |
| 9 | 24 | |
| 10 | 7 | |
| 11 | Evaluation Dataset for Zero Pronoun in Japanese to English Translation | 4 |
| 12 | 22 | |
| 13 | 2 | |
| 14 | 4 | |
| 15 | 11 | |
| 16 | Reducing Odd Generation from Neural Headline Generation | 3 |
| 17 | 17 | |
| 18 | Handling Multiword Expressions in Causality Estimation | 16 |
| 19 | 5 | |
| 20 | Detecting Chronic Critics Based on Sentiment Polarity and User’s Behavior in Social Media | 1 |
About Sho Takase
Sho Takase is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems, having authored 31 papers that have together received 316 indexed citations. Recurring topics across this work include Topic Modeling (27 papers), Natural Language Processing Techniques (25 papers) and Advanced Text Analysis Techniques (7 papers). The work is most often cited by research in Artificial Intelligence (286 citations), Computer Vision and Pattern Recognition (52 citations) and Health Informatics (2 citations). Sho Takase has collaborated with scholars based in Japan and United States. Frequent co-authors include Naoaki Okazaki, Jun Suzuki, Masaaki Nagata, Tsutomu Hirao, Shun Kiyono, Kentaro Inui, Atsushi Keyaki, Naoya Inoue, Masahiro Kaneko and Hidetaka Kamigaito. Their work appears in journals such as Engineering Applications of Artificial Intelligence, Language Resources and Evaluation and ACM Transactions on Asian and Low-Resource Language Information 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.