Fei Huang
- Artificial Intelligence top 0.2%
- Topic Modeling 120
- Natural Language Processing Techniques 105
- Speech and dialogue systems 14
- Advanced Text Analysis Techniques 12
- Domain Adaptation and Few-Shot Learning 10
- Text Readability and Simplification 9
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- Multimodal Machine Learning Applications 47
- Health Informatics top 5%
- Information Systems top 2%
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- Biomedical Text Mining and Ontologies 8
- Co-authors
- Luo SiChuanqi TanHuajun ChenNingyu ZhangStephan VogelAlexander YatesNguyễn BáchShumin Deng
- Journals
- Transactions of the Association for Computational Linguistics (3 papers)Pattern Recognition (3 papers)Knowledge-Based Systems (2 papers)
- Partner nations
- ChinaUnited StatesCayman Islands
In The Last Decade
Fei Huang
150 papers receiving 3.2k citations
Hit Papers
Peers
Comparison fields: 5 of 114
- Artificial Intelligence 2.9k
- Computer Vision and Pattern Recognition 684
- Health Informatics 28
- Management Science and Operations Research 233
- Information Systems 338
Countries citing papers authored by Fei Huang
This map shows the geographic impact of Fei Huang'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 Fei Huang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fei Huang more than expected).
Fields of papers citing papers by Fei Huang
This network shows the impact of papers produced by Fei Huang. 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 Fei Huang. The network helps show where Fei Huang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Fei Huang, 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 | 2025 | 0 | |
| 2 | 2024 | 6 | |
| 3 | 2024 | 4 | |
| 4 | 2024 | 2 | |
| 5 | 2024 | 3 | |
| 6 | 2024 | 3 | |
| 7 | 2023 | 61 | |
| 8 | 2023 | 3 | |
| 9 | 2023 | 4 | |
| 10 | 2023 | 7 | |
| 11 | 2023 | 10 | |
| 12 | 2022 | 32 | |
| 13 | 2021 | 17 | |
| 14 | 2021 | 42 | |
| 15 | A Joint Neural Model for Information Extraction with Global Featuresbreakdown → | 2020 | 225 |
| 16 | 2020 | 4 | |
| 17 | 2016 | 13 | |
| 18 | 2013 | 9 | |
| 19 | Biased Representation Learning for Domain Adaptation | 2012 | 13 |
| 20 | Hierarchical System Combination for Machine Translation | 2007 | 26 |
About Fei Huang
Fei Huang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Human-Computer Interaction and Management Science and Operations Research, having authored 162 papers that have together received 3.4k indexed citations. Recurring topics across this work include Topic Modeling (120 papers), Natural Language Processing Techniques (105 papers), Multimodal Machine Learning Applications (47 papers), Speech and dialogue systems (14 papers), Advanced Text Analysis Techniques (12 papers), Domain Adaptation and Few-Shot Learning (10 papers), Text Readability and Simplification (9 papers) and Biomedical Text Mining and Ontologies (8 papers). The work is most often cited by research in Artificial Intelligence (2.9k citations), Computer Vision and Pattern Recognition (684 citations), Health Informatics (28 citations), Management Science and Operations Research (233 citations) and Information Systems (338 citations). Fei Huang has collaborated with scholars based in China, United States and Cayman Islands. Frequent co-authors include Luo Si, Chuanqi Tan, Huajun Chen, Ningyu Zhang, Stephan Vogel, Alexander Yates, Nguyễn Bách, Shumin Deng, Songfang Huang and Ying Lin. Their work appears in journals such as Transactions of the Association for Computational Linguistics, Pattern Recognition, Knowledge-Based Systems, IEEE/ACM Transactions on Audio Speech and Language Processing and Computational Linguistics.
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.