Jonathan Graehl
Impact in
- Artificial Intelligence top 1%
- Natural Language Processing Techniques
- Topic Modeling
- Algorithms and Data Compression
- Speech and dialogue systems
- Text Readability and Simplification
- Semantic Web and Ontologies
- Speech Recognition and Synthesis
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- Handwritten Text Recognition Techniques
Papers in
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- Natural Language Processing Techniques 5
- Topic Modeling 2
- Algorithms and Data Compression 2
- Machine Learning and Algorithms 1
- Text Readability and Simplification 1
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- Handwritten Text Recognition Techniques 3
- Co-authors
- Kevin Knight (6 shared papers)Wei Wang (1 shared paper)Steve DeNeefe (1 shared paper)Michel Galley (1 shared paper)Daniel Marcu (1 shared paper)Andreas Maletti (1 shared paper)Mark Hopkins (1 shared paper)Sujith Ravi (1 shared paper)
- Journals
- Computational Linguistics (1 paper)SIAM Journal on Computing (1 paper)North American Chapter of the Association for Computational Linguistics (1 paper)The COCOON platform (University of Paris) (1 paper)Columbia Academic Commons (Columbia University) (1 paper)
- Partner nations
- United States
In The Last Decade
Jonathan Graehl
6 papers receiving 755 citations
Peers
Comparison fields: 5 of 30
- Artificial Intelligence 880
- Computer Vision and Pattern Recognition 141
- Computational Theory and Mathematics 60
- Information Systems 70
- Language and Linguistics 29
Countries citing papers authored by Jonathan Graehl
This map shows the geographic impact of Jonathan Graehl'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 Jonathan Graehl with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonathan Graehl more than expected).
Fields of papers citing papers by Jonathan Graehl
This network shows the impact of papers produced by Jonathan Graehl. 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 Jonathan Graehl. The network helps show where Jonathan Graehl may publish in the future.
Co-authors
The 10 scholars most cited alongside Jonathan Graehl, 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 | 1997 | 334 | |
| 2 | 2006 | 310 | |
| 3 | 1997 | 129 | |
| 4 | 2008 | 63 | |
| 5 | 2009 | 48 | |
| 6 | Bayesian Inference for Finite-State Transducers | 2010 | 15 |
About Jonathan Graehl
Jonathan Graehl is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Theory and Mathematics, Infectious Diseases and Organic Chemistry, having authored 6 papers that have together received 899 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (5 papers), Handwritten Text Recognition Techniques (3 papers), Topic Modeling (2 papers), Algorithms and Data Compression (2 papers), Mathematics, Computing, and Information Processing (2 papers), Machine Learning and Algorithms (1 paper), Text Readability and Simplification (1 paper) and semigroups and automata theory (1 paper). The work is most often cited by research in Artificial Intelligence (880 citations), Computer Vision and Pattern Recognition (141 citations), Computational Theory and Mathematics (60 citations), Information Systems (70 citations) and Language and Linguistics (29 citations). Jonathan Graehl has collaborated with scholars based in United States. Frequent co-authors include Kevin Knight, Wei Wang, Steve DeNeefe, Michel Galley, Daniel Marcu, Andreas Maletti, Mark Hopkins, Sujith Ravi, David Chiang and Adam Pauls. Their work appears in journals such as Computational Linguistics, SIAM Journal on Computing, North American Chapter of the Association for Computational Linguistics, The COCOON platform (University of Paris) and Columbia Academic Commons (Columbia University).
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.