Duyu Tang
- Artificial Intelligence top 0.05%
- Information Systems top 0.2%
- Software top 0.5%
- Computer Vision and Pattern Recognition top 2%
- Signal Processing top 1%
- Topics
- Topic Modeling (53 papers)Natural Language Processing Techniques (31 papers)Sentiment Analysis and Opinion Mining (18 papers)
- Journals
- NeurocomputingIEEE Transactions on Knowledge and Data EngineeringMultimedia Tools and Applications
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Duyu Tang
58 papers receiving 7.5k citations
Hit Papers
Peers
Comparison fields: 5 of 128
- Artificial Intelligence 6.3k
- Information Systems 2.2k
- Software 645
- Computer Vision and Pattern Recognition 598
- Signal Processing 554
Countries citing papers authored by Duyu Tang
This map shows the geographic impact of Duyu Tang'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 Duyu Tang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Duyu Tang more than expected).
Fields of papers citing papers by Duyu Tang
This network shows the impact of papers produced by Duyu Tang. 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 Duyu Tang. The network helps show where Duyu Tang may publish in the future.
Co-authorship network of co-authors of Duyu Tang
This figure shows the co-authorship network connecting the top 25 collaborators of Duyu Tang. A scholar is included among the top collaborators of Duyu Tang 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 Duyu Tang. Duyu Tang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 16 | |
| 7 | 120 | |
| 8 | K-Adapter: Infusing Knowledge into Pre-Trained Models with Adaptersbreakdown → | 218 |
| 9 | 20 | |
| 10 | 25 | |
| 11 | 100 | |
| 12 | 54 | |
| 13 | Dialog-to-action: conversational question answering over a large-scale knowledge base | 43 |
| 14 | 163 | |
| 15 | Aspect Level Sentiment Classification with Deep Memory Networkbreakdown → | 675 |
| 16 | English-Chinese Knowledge Base Translation with Neural Network | 7 |
| 17 | Effective LSTMs for Target-Dependent Sentiment Classification | 249 |
| 18 | User modeling with neural network for review rating prediction | 87 |
| 19 | Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classificationbreakdown → | 708 |
| 20 | Building Large-Scale Twitter-Specific Sentiment Lexicon : A Representation Learning Approach | 122 |
About Duyu Tang
Duyu Tang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems, having authored 59 papers that have together received 7.8k indexed citations. Recurring topics across this work include Topic Modeling (53 papers), Natural Language Processing Techniques (31 papers) and Sentiment Analysis and Opinion Mining (18 papers). The work is most often cited by research in Artificial Intelligence (6.3k citations), Software (645 citations) and Information Systems (2.2k citations). Duyu Tang has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Bing Qin, Ting Liu, Ming Zhou, Furu Wei, Ting Liu, Nan Duan, Xiaocheng Feng, Daxin Jiang, Nan Yang and Daya Guo. Their work appears in journals such as Neurocomputing, IEEE Transactions on Knowledge and Data Engineering and Multimedia Tools and Applications.
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.