Jon Gauthier
Impact in
- Artificial Intelligence top 5%
- Topic Modeling
- Natural Language Processing Techniques
- Text Readability and Simplification
- Advanced Text Analysis Techniques
- Speech and dialogue systems
- Speech Recognition and Synthesis
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- Multimodal Machine Learning Applications
Papers in
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- Topic Modeling 7
- Natural Language Processing Techniques 6
- Speech Recognition and Synthesis 3
- Text Readability and Simplification 2
- Neural Networks and Applications 1
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- Multimodal Machine Learning Applications 1
- Co-authors
- Roger Lévy (7 shared papers)Raghav Gupta (1 shared paper)Christopher D. Manning (1 shared paper)Christopher Potts (1 shared paper)Samuel R. Bowman (1 shared paper)Abhinav Rastogi (1 shared paper)Li Lucy (1 shared paper)Jennifer Hu (3 shared papers)
- Journals
- Cognitive Science (1 paper)World Literature Today (1 paper)DSpace@MIT (Massachusetts Institute of Technology) (1 paper)
- Partner nations
- United States
In The Last Decade
Jon Gauthier
9 papers receiving 243 citations
Peers
Comparison fields: 5 of 41
- Artificial Intelligence 231
- Computer Vision and Pattern Recognition 61
- General Social Sciences 7
- Cognitive Neuroscience 38
- Health Informatics 2
Countries citing papers authored by Jon Gauthier
This map shows the geographic impact of Jon Gauthier'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 Jon Gauthier with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jon Gauthier more than expected).
Fields of papers citing papers by Jon Gauthier
This network shows the impact of papers produced by Jon Gauthier. 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 Jon Gauthier. The network helps show where Jon Gauthier may publish in the future.
Co-authors
The 15 scholars most cited alongside Jon Gauthier, 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 | 2016 | 139 | |
| 2 | 2019 | 39 | |
| 3 | 2020 | 26 | |
| 4 | 2017 | 21 | |
| 5 | A Systematic Assessment of Syntactic Generalization in Neural Language Models | 2020 | 18 |
| 6 | 1990 | 14 | |
| 7 | 2023 | 6 | |
| 8 | 2023 | 3 | |
| 9 | On the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behavior. | 2020 | 1 |
| 10 | 2023 | 0 |
About Jon Gauthier
Jon Gauthier is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Cognitive Neuroscience, Cultural Studies and Infectious Diseases, having authored 10 papers that have together received 267 indexed citations. Recurring topics across this work include Topic Modeling (7 papers), Natural Language Processing Techniques (6 papers), Speech Recognition and Synthesis (3 papers), Text Readability and Simplification (2 papers), Neurobiology of Language and Bilingualism (1 paper), Language and cultural evolution (1 paper), Multimodal Machine Learning Applications (1 paper) and Neural Networks and Applications (1 paper). The work is most often cited by research in Artificial Intelligence (231 citations), Computer Vision and Pattern Recognition (61 citations), General Social Sciences (7 citations), Cognitive Neuroscience (38 citations) and Health Informatics (2 citations). Jon Gauthier has collaborated with scholars based in United States. Frequent co-authors include Roger Lévy, Raghav Gupta, Christopher D. Manning, Christopher Potts, Samuel R. Bowman, Abhinav Rastogi, Li Lucy, Jennifer Hu, Peng Qian and Ethan Wilcox. Their work appears in journals such as Cognitive Science, World Literature Today and DSpace@MIT (Massachusetts Institute of Technology).
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.