Tom Young
Impact in
- Artificial Intelligence top 0.5%
- Topic Modeling
- Natural Language Processing Techniques
- Speech and dialogue systems
- Sentiment Analysis and Opinion Mining
- Advanced Text Analysis Techniques
- Text and Document Classification Technologies
- Anomaly Detection Techniques and Applications
- Health Informatics top 5%
Papers in
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- Topic Modeling 9
- Natural Language Processing Techniques 7
- Speech and dialogue systems 6
- Text and Document Classification Technologies 1
- AI in Service Interactions 1
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- Music and Audio Processing 1
- Co-authors
- Erik Cambria (8 shared papers)Soujanya Poria (2 shared papers)Devamanyu Hazarika (1 shared paper)Minlie Huang (2 shared papers)Hao Zhou (2 shared papers)Xiaoyan Zhu (1 shared paper)Jingfang Xu (1 shared paper)Haizhou Zhao (1 shared paper)
In The Last Decade
Tom Young
10 papers receiving 3.0k citations
Tom Young's Hit Papers
Peers
Comparison fields: 5 of 172
- Artificial Intelligence 2.0k
- Health Informatics 37
- Computer Vision and Pattern Recognition 521
- Signal Processing 168
- Information Systems 278
Countries citing papers authored by Tom Young
This map shows the geographic impact of Tom Young'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 Tom Young with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tom Young more than expected).
Fields of papers citing papers by Tom Young
This network shows the impact of papers produced by Tom Young. 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 Tom Young. The network helps show where Tom Young may publish in the future.
Co-authors
The 15 scholars most cited alongside Tom Young, 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 | Recent Trends in Deep Learning Based Natural Language Processing [Review Article] Hit paper breakdown → | 2018 | 2299 |
| 2 | Commonsense Knowledge Aware Conversation Generation with Graph Attention Hit paper breakdown → | 2018 | 362 |
| 3 | 2018 | 194 | |
| 4 | 2022 | 150 | |
| 5 | 2022 | 38 | |
| 6 | 2020 | 32 | |
| 7 | 2022 | 22 | |
| 8 | 2021 | 6 | |
| 9 | Recent Advances in Deep Learning-based Dialogue Systems | 2021 | 3 |
| 10 | 2019 | 1 |
About Tom Young
Tom Young is a scholar working on Artificial Intelligence, Signal Processing, Statistical and Nonlinear Physics, Computer Vision and Pattern Recognition and Control and Systems Engineering, having authored 10 papers that have together received 3.1k indexed citations. Recurring topics across this work include Topic Modeling (9 papers), Natural Language Processing Techniques (7 papers), Speech and dialogue systems (6 papers), Robotic Mechanisms and Dynamics (1 paper), Text and Document Classification Technologies (1 paper), AI in Service Interactions (1 paper), Music and Audio Processing (1 paper) and Mechanics and Biomechanics Studies (1 paper). The work is most often cited by research in Artificial Intelligence (2.0k citations), Health Informatics (37 citations), Computer Vision and Pattern Recognition (521 citations), Signal Processing (168 citations) and Information Systems (278 citations). Tom Young has collaborated with scholars based in Singapore, China and Italy. Frequent co-authors include Erik Cambria, Soujanya Poria, Devamanyu Hazarika, Minlie Huang, Hao Zhou, Xiaoyan Zhu, Jingfang Xu, Haizhou Zhao, Vlad Pandelea and Jinjie Ni. Their work appears in journals such as Neural Computing and Applications, IEEE Computational Intelligence Magazine, Neurocomputing, Artificial Intelligence Review and Faraday Discussions.
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.