R. Thomas McCoy
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition
- Cognitive Neuroscience
- Cultural Studies top 10%
- Information Systems
- Co-authors
- Tal LinzenDipanjan DasEmily PitlerPaul SmolenskyJianfeng GaoRobert FrankBenjamin Van DurmeBerlin Chen
- Topics
- Topic Modeling (12 papers)Natural Language Processing Techniques (12 papers)Text Readability and Simplification (4 papers)
- Journals
- Proceedings of the National Academy of SciencesNature CommunicationsCurrent Directions in Psychological Science
- Partner nations
- United StatesUnited KingdomSwitzerland
In The Last Decade
R. Thomas McCoy
17 papers receiving 318 citations
Peers
Comparison fields: 5 of 60
- Artificial Intelligence 283
- Computer Vision and Pattern Recognition 56
- Cognitive Neuroscience 28
- Cultural Studies 22
- Information Systems 15
Countries citing papers authored by R. Thomas McCoy
This map shows the geographic impact of R. Thomas McCoy'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 R. Thomas McCoy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites R. Thomas McCoy more than expected).
Fields of papers citing papers by R. Thomas McCoy
This network shows the impact of papers produced by R. Thomas McCoy. 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 R. Thomas McCoy. The network helps show where R. Thomas McCoy may publish in the future.
Co-authorship network of co-authors of R. Thomas McCoy
This figure shows the co-authorship network connecting the top 25 collaborators of R. Thomas McCoy. A scholar is included among the top collaborators of R. Thomas McCoy based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with R. Thomas McCoy. R. Thomas McCoy is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 28 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 23 | |
| 6 | 10 | |
| 7 | 27 | |
| 8 | 2 | |
| 9 | 76 | |
| 10 | 1 | |
| 11 | 63 | |
| 12 | 67 | |
| 13 | Revisiting the poverty of the stimulus: hierarchical generalization without a hierarchical bias in recurrent neural networks. | 16 |
| 14 | Looking for ELMo's friends: Sentence-Level Pretraining Beyond Language Modeling. | 11 |
| 15 | 6 | |
| 16 | Linguistically Rich Vector Representations of Supertags for TAG Parsing | 4 |
| 17 | 1 | |
| 18 | 1 |
About R. Thomas McCoy
R. Thomas McCoy is a scholar working on Artificial Intelligence, Linguistics and Language and Cultural Studies, having authored 18 papers that have together received 340 indexed citations. Recurring topics across this work include Topic Modeling (12 papers), Natural Language Processing Techniques (12 papers) and Text Readability and Simplification (4 papers). The work is most often cited by research in Artificial Intelligence (283 citations), Health Informatics (7 citations) and General Social Sciences (10 citations). R. Thomas McCoy has collaborated with scholars based in United States, United Kingdom and Switzerland. Frequent co-authors include Tal Linzen, Dipanjan Das, Emily Pitler, Paul Smolensky, Jianfeng Gao, Robert Frank, Benjamin Van Durme, Berlin Chen, Thomas L. Griffiths and Ian Tenney. Their work appears in journals such as Proceedings of the National Academy of Sciences, Nature Communications and Current Directions in Psychological Science.
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.