Jon Gauthier
Impact in
- Artificial Intelligence top 5%
- Topic Modeling
- Natural Language Processing Techniques
- Text Readability and Simplification
- Speech and dialogue systems
- Advanced Text Analysis Techniques
- Domain Adaptation and Few-Shot Learning
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- Multimodal Machine Learning Applications
Papers in
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- Topic Modeling 8
- Natural Language Processing Techniques 7
- Speech Recognition and Synthesis 3
- Text Readability and Simplification 2
- Neural Networks and Applications 1
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- Neurobiology of Language and Bilingualism 2
- Co-authors
- Roger Lévy (9 shared papers)Christopher D. Manning (1 shared paper)Abhinav Rastogi (1 shared paper)Raghav Gupta (1 shared paper)Samuel R. Bowman (1 shared paper)Christopher Potts (1 shared paper)Li Lucy (1 shared paper)Ethan Wilcox (4 shared papers)
- Journals
- Cognitive Science (2 papers)World Literature Today (1 paper)DSpace@MIT (Massachusetts Institute of Technology) (2 papers)
- Partner nations
- United States
In The Last Decade
Jon Gauthier
11 papers receiving 274 citations
Peers
Comparison fields: 5 of 45
- Artificial Intelligence 259
- Computer Vision and Pattern Recognition 64
- Cognitive Neuroscience 41
- General Social Sciences 7
- 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 17 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 | 156 | |
| 2 | 2019 | 41 | |
| 3 | 2020 | 31 | |
| 4 | 2017 | 25 | |
| 5 | A Systematic Assessment of Syntactic Generalization in Neural Language Models | 2020 | 19 |
| 6 | 1990 | 15 | |
| 7 | 2023 | 6 | |
| 8 | 2023 | 4 | |
| 9 | 2022 | 2 | |
| 10 | Word learning and the acquisition of a syntactic–semantic overhypothesis | 2018 | 1 |
| 11 | On the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behavior. | 2020 | 1 |
| 12 | 2023 | 1 |
About Jon Gauthier
Jon Gauthier is a scholar working on Artificial Intelligence, Cognitive Neuroscience, Computer Vision and Pattern Recognition, Cultural Studies and Infectious Diseases, having authored 12 papers that have together received 302 indexed citations. Recurring topics across this work include Topic Modeling (8 papers), Natural Language Processing Techniques (7 papers), Speech Recognition and Synthesis (3 papers), Text Readability and Simplification (2 papers), Neurobiology of Language and Bilingualism (2 papers), Language and cultural evolution (1 paper), Neural Networks and Applications (1 paper) and Multimodal Machine Learning Applications (1 paper). The work is most often cited by research in Artificial Intelligence (259 citations), Computer Vision and Pattern Recognition (64 citations), Cognitive Neuroscience (41 citations), General Social Sciences (7 citations) and Health Informatics (2 citations). Jon Gauthier has collaborated with scholars based in United States. Frequent co-authors include Roger Lévy, Christopher D. Manning, Abhinav Rastogi, Raghav Gupta, Samuel R. Bowman, Christopher Potts, Li Lucy, Ethan Wilcox, Peng Qian and Jennifer Hu. 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.