Ke Tran
Impact in
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Text Readability and Simplification
- Speech Recognition and Synthesis
- Speech and dialogue systems
- Sentiment Analysis and Opinion Mining
- Advanced Text Analysis Techniques
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- Multimodal Machine Learning Applications
Papers in
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- Natural Language Processing Techniques 12
- Topic Modeling 11
- Text Readability and Simplification 3
- Advanced Text Analysis Techniques 1
- Speech Recognition and Synthesis 1
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- Multimodal Machine Learning Applications 4
- Handwritten Text Recognition Techniques 1
- Co-authors
- Arianna Bisazza (5 shared papers)Christof Monz (5 shared papers)Yonatan Bisk (2 shared papers)Kristina Toutanova (1 shared paper)Chris Brockett (1 shared paper)Saleema Amershi (1 shared paper)Daniel Marcu (1 shared paper)Kevin Knight (1 shared paper)
- Journals
- UvA-DARE (University of Amsterdam) (7 papers)University of Groningen research database (University of Groningen / Centre for Information Technology) (1 paper)Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (1 paper)
- Partner nations
- NetherlandsUnited StatesGermany
In The Last Decade
Ke Tran
12 papers receiving 176 citations
Peers
Comparison fields: 5 of 32
- Artificial Intelligence 180
- Computer Vision and Pattern Recognition 61
- Health Informatics 1
- Signal Processing 7
- Information Systems 14
Countries citing papers authored by Ke Tran
This map shows the geographic impact of Ke Tran'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 Ke Tran with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ke Tran more than expected).
Fields of papers citing papers by Ke Tran
This network shows the impact of papers produced by Ke Tran. 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 Ke Tran. The network helps show where Ke Tran may publish in the future.
Co-authors
The 17 scholars most cited alongside Ke Tran, 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 | 2018 | 64 | |
| 2 | 2016 | 40 | |
| 3 | 2016 | 28 | |
| 4 | 2016 | 25 | |
| 5 | 2019 | 14 | |
| 6 | 2014 | 9 | |
| 7 | 2018 | 8 | |
| 8 | 2020 | 3 | |
| 9 | 2021 | 3 | |
| 10 | A distributed inflection model for translating into morphologically rich languages | 2015 | 2 |
| 11 | 2024 | 1 | |
| 12 | 2022 | 1 |
About Ke Tran
Ke Tran is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Language and Linguistics, Infectious Diseases and Organic Chemistry, having authored 12 papers that have together received 198 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (12 papers), Topic Modeling (11 papers), Multimodal Machine Learning Applications (4 papers), Text Readability and Simplification (3 papers), Advanced Text Analysis Techniques (1 paper), Handwritten Text Recognition Techniques (1 paper), Translation Studies and Practices (1 paper) and Speech Recognition and Synthesis (1 paper). The work is most often cited by research in Artificial Intelligence (180 citations), Computer Vision and Pattern Recognition (61 citations), Health Informatics (1 citation), Signal Processing (7 citations) and Information Systems (14 citations). Ke Tran has collaborated with scholars based in Netherlands, United States and Germany. Frequent co-authors include Arianna Bisazza, Christof Monz, Yonatan Bisk, Kristina Toutanova, Chris Brockett, Saleema Amershi, Daniel Marcu, Kevin Knight, Ashish Vaswani and Ming Tan. Their work appears in journals such as UvA-DARE (University of Amsterdam), University of Groningen research database (University of Groningen / Centre for Information Technology) and Proceedings of the 2021 Conference on Empirical Methods in 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.