Jeff Da
Impact in
- Artificial Intelligence top 5%
- Topic Modeling
- Natural Language Processing Techniques
- Advanced Graph Neural Networks
- Advanced Text Analysis Techniques
- Speech and dialogue systems
- Sentiment Analysis and Opinion Mining
- Explainable Artificial Intelligence (XAI)
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- Multimodal Machine Learning Applications
Papers in
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- Topic Modeling 5
- Natural Language Processing Techniques 4
- Advanced Graph Neural Networks 2
- Sentiment Analysis and Opinion Mining 1
- Advanced Text Analysis Techniques 1
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- Humor Studies and Applications 1
- Co-authors
- Yejin Choi (4 shared papers)Jena D. Hwang (3 shared papers)Antoine Bosselut (3 shared papers)Ronan Le Bras (1 shared paper)Chandra Bhagavatula (1 shared paper)Keisuke Sakaguchi (1 shared paper)Jungo Kasai (1 shared paper)Rowan Zellers (2 shared papers)
- Partner nations
- United States
In The Last Decade
Jeff Da
5 papers receiving 258 citations
Jeff Da's Hit Papers
Peers
Comparison fields: 5 of 37
- Artificial Intelligence 238
- Computer Vision and Pattern Recognition 66
- General Social Sciences 6
- Management Science and Operations Research 14
- Experimental and Cognitive Psychology 13
Countries citing papers authored by Jeff Da
This map shows the geographic impact of Jeff Da'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 Jeff Da with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jeff Da more than expected).
Fields of papers citing papers by Jeff Da
This network shows the impact of papers produced by Jeff Da. 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 Jeff Da. The network helps show where Jeff Da may publish in the future.
Co-authors
The 17 scholars most cited alongside Jeff Da, 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 | (Comet-) Atomic 2020: On Symbolic and Neural Commonsense Knowledge Graphs Hit paper breakdown → | 2021 | 212 |
| 2 | 2023 | 21 | |
| 3 | 2021 | 15 | |
| 4 | 2019 | 13 | |
| 5 | 2021 | 8 | |
| 6 | 2019 | 1 |
About Jeff Da
Jeff Da is a scholar working on Artificial Intelligence, Social Psychology, Sociology and Political Science, Computer Vision and Pattern Recognition and Experimental and Cognitive Psychology, having authored 6 papers that have together received 270 indexed citations. Recurring topics across this work include Topic Modeling (5 papers), Natural Language Processing Techniques (4 papers), Advanced Graph Neural Networks (2 papers), Sentiment Analysis and Opinion Mining (1 paper), Language, Metaphor, and Cognition (1 paper), Misinformation and Its Impacts (1 paper), Advanced Text Analysis Techniques (1 paper) and Humor Studies and Applications (1 paper). The work is most often cited by research in Artificial Intelligence (238 citations), Computer Vision and Pattern Recognition (66 citations), General Social Sciences (6 citations), Management Science and Operations Research (14 citations) and Experimental and Cognitive Psychology (13 citations). Jeff Da has collaborated with scholars based in United States. Frequent co-authors include Yejin Choi, Jena D. Hwang, Antoine Bosselut, Ronan Le Bras, Chandra Bhagavatula, Keisuke Sakaguchi, Jungo Kasai, Rowan Zellers, Jack Hessel and Lillian Lee.
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.