Jay DeYoung
Impact in
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Advanced Text Analysis Techniques
- Text and Document Classification Technologies
Papers in
-
- Natural Language Processing Techniques 8
- Topic Modeling 8
- Advanced Text Analysis Techniques 2
- Speech and dialogue systems 1
- Semantic Web and Ontologies 1
-
- Biomedical Text Mining and Ontologies 4
- Co-authors
- Bailey Kuehl (2 shared papers)Lucy Lu Wang (2 shared papers)Madeleine van Zuylen (1 shared paper)Iz Beltagy (1 shared paper)Jonathan W. Mink (1 shared paper)Frederick J. Marshall (1 shared paper)Elisabeth A. de Blieck (1 shared paper)Leon Dure (1 shared paper)
- Journals
- Neurology (1 paper)Machine Translation (1 paper)Transactions of the Association for Computational Linguistics (1 paper)JAMIA Open (1 paper)Journal of the Optical Society of America (1 paper)
- Partner nations
- United StatesMexicoChina
In The Last Decade
Jay DeYoung
12 papers receiving 185 citations
Peers
Comparison fields: 5 of 54
- Health Informatics 8
- Artificial Intelligence 99
- Physiology 59
- Physiology 7
- Cell Biology 22
Countries citing papers authored by Jay DeYoung
This map shows the geographic impact of Jay DeYoung'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 Jay DeYoung with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jay DeYoung more than expected).
Fields of papers citing papers by Jay DeYoung
This network shows the impact of papers produced by Jay DeYoung. 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 Jay DeYoung. The network helps show where Jay DeYoung may publish in the future.
Co-authors
The 25 scholars most cited alongside Jay DeYoung, 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 | 2005 | 77 | |
| 2 | 2021 | 50 | |
| 3 | 2019 | 16 | |
| 4 | 2017 | 11 | |
| 5 | PARMA: A Predicate Argument Aligner | 2013 | 9 |
| 6 | 2023 | 8 | |
| 7 | 1957 | 7 | |
| 8 | 2024 | 6 | |
| 9 | 2016 | 6 | |
| 10 | 2015 | 3 | |
| 11 | 2024 | 2 | |
| 12 | 2011 | 1 |
About Jay DeYoung
Jay DeYoung is a scholar working on Artificial Intelligence, Molecular Biology, Structural Biology, Radiation and Media Technology, having authored 12 papers that have together received 196 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (8 papers), Topic Modeling (8 papers), Biomedical Text Mining and Ontologies (4 papers), Advanced Text Analysis Techniques (2 papers), Speech and dialogue systems (1 paper), Semantic Web and Ontologies (1 paper), Advanced Electron Microscopy Techniques and Applications (1 paper) and Image Processing Techniques and Applications (1 paper). The work is most often cited by research in Health Informatics (8 citations), Artificial Intelligence (99 citations), Physiology (59 citations), Physiology (7 citations) and Cell Biology (22 citations). Jay DeYoung has collaborated with scholars based in United States, Mexico and China. Frequent co-authors include Bailey Kuehl, Lucy Lu Wang, Madeleine van Zuylen, Iz Beltagy, Jonathan W. Mink, Frederick J. Marshall, Elisabeth A. de Blieck, Leon Dure, Paul G. Rothberg and Denia Ramirez‐Montealegre. Their work appears in journals such as Neurology, Machine Translation, Transactions of the Association for Computational Linguistics, JAMIA Open and Journal of the Optical Society of America.
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.