Travis Wolfe
Impact in
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- Topic Modeling
- Natural Language Processing Techniques
- Advanced Text Analysis Techniques
- Semantic Web and Ontologies
- Advanced Graph Neural Networks
Papers in
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- Topic Modeling 6
- Natural Language Processing Techniques 5
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- Biomedical Text Mining and Ontologies 2
- Co-authors
- Mark Dredze (6 shared papers)Benjamin Van Durme (5 shared papers)Chris Callison-Burch (2 shared papers)Ellie Pavlick (1 shared paper)Pushpendre Rastogi (1 shared paper)Jaime Carbonell (1 shared paper)Rebecca Knowles (1 shared paper)Matthew R. Gormley (2 shared papers)
- Journals
- Clinical Chemistry (1 paper)Figshare (1 paper)Meeting of the Association for Computational Linguistics (1 paper)
- Partner nations
- United States
In The Last Decade
Travis Wolfe
7 papers receiving 52 citations
Peers
Comparison fields: 5 of 16
- Artificial Intelligence 60
- Health Informatics 1
- Computer Science Applications 4
- Computer Vision and Pattern Recognition 13
- Information Systems 9
Countries citing papers authored by Travis Wolfe
This map shows the geographic impact of Travis Wolfe'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 Travis Wolfe with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Travis Wolfe more than expected).
Fields of papers citing papers by Travis Wolfe
This network shows the impact of papers produced by Travis Wolfe. 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 Travis Wolfe. The network helps show where Travis Wolfe may publish in the future.
Co-authors
The 19 scholars most cited alongside Travis Wolfe, 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 | 2015 | 24 | |
| 2 | PARMA: A Predicate Argument Aligner | 2013 | 10 |
| 3 | Topic Models and Metadata for Visualizing Text Corpora | 2013 | 10 |
| 4 | 2018 | 6 | |
| 5 | 2015 | 6 | |
| 6 | 2015 | 3 | |
| 7 | 2017 | 3 | |
| 8 | 1978 | 0 |
About Travis Wolfe
Travis Wolfe is a scholar working on Artificial Intelligence, Molecular Biology, Information Systems, Computer Vision and Pattern Recognition and Health Information Management, having authored 8 papers that have together received 62 indexed citations. Recurring topics across this work include Topic Modeling (6 papers), Natural Language Processing Techniques (5 papers), Web Data Mining and Analysis (2 papers), Biomedical Text Mining and Ontologies (2 papers), Data Management and Algorithms (1 paper), Electronic Health Records Systems (1 paper), Handwritten Text Recognition Techniques (1 paper) and Video Analysis and Summarization (1 paper). The work is most often cited by research in Artificial Intelligence (60 citations), Health Informatics (1 citation), Computer Science Applications (4 citations), Computer Vision and Pattern Recognition (13 citations) and Information Systems (9 citations). Travis Wolfe has collaborated with scholars based in United States. Frequent co-authors include Mark Dredze, Benjamin Van Durme, Chris Callison-Burch, Ellie Pavlick, Pushpendre Rastogi, Jaime Carbonell, Rebecca Knowles, Matthew R. Gormley, Eugene Fink and Anatole Gershman. Their work appears in journals such as Clinical Chemistry, Figshare and Meeting of the Association for 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.