Ellie Pavlick
- Artificial Intelligence top 0.5%
- Computer Vision and Pattern Recognition top 2%
- Information Systems top 5%
- Cognitive Neuroscience
- Sociology and Political Science
- Co-authors
- Ian TenneyDipanjan DasChris Callison-BurchTal LinzenBenjamin Van DurmeAlbert WebsonQuan Ze ChenWei Xu
- Topics
- Topic Modeling (45 papers)Natural Language Processing Techniques (45 papers)Multimodal Machine Learning Applications (15 papers)
- Partner nations
- United StatesFranceGermany
In The Last Decade
Ellie Pavlick
65 papers receiving 2.8k citations
Hit Papers
Peers
Comparison fields: 5 of 124
- Artificial Intelligence 2.5k
- Computer Vision and Pattern Recognition 540
- Information Systems 223
- Cognitive Neuroscience 93
- Sociology and Political Science 89
Countries citing papers authored by Ellie Pavlick
This map shows the geographic impact of Ellie Pavlick'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 Ellie Pavlick with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ellie Pavlick more than expected).
Fields of papers citing papers by Ellie Pavlick
This network shows the impact of papers produced by Ellie Pavlick. 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 Ellie Pavlick. The network helps show where Ellie Pavlick may publish in the future.
Co-authorship network of co-authors of Ellie Pavlick
This figure shows the co-authorship network connecting the top 25 collaborators of Ellie Pavlick. A scholar is included among the top collaborators of Ellie Pavlick 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 Ellie Pavlick. Ellie Pavlick 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 | 0 | |
| 3 | 2 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 0 | |
| 7 | 0 | |
| 8 | 2 | |
| 9 | 4 | |
| 10 | 2 | |
| 11 | 124 | |
| 12 | BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelbreakdown → | 174 |
| 13 | 14 | |
| 14 | 5 | |
| 15 | Inherent Disagreements in Human Textual Inferences | 2 |
| 16 | 5 | |
| 17 | Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inferencebreakdown → | 471 |
| 18 | Towards a Unified Natural Language Inference Framework to Evaluate Sentence Representations | 4 |
| 19 | Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL 2015) | 26 |
| 20 | 171 |
About Ellie Pavlick
Ellie Pavlick is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Science Applications, having authored 72 papers that have together received 2.9k indexed citations. Recurring topics across this work include Topic Modeling (45 papers), Natural Language Processing Techniques (45 papers) and Multimodal Machine Learning Applications (15 papers). The work is most often cited by research in Artificial Intelligence (2.5k citations), Health Informatics (56 citations) and Computer Vision and Pattern Recognition (540 citations). Ellie Pavlick has collaborated with scholars based in United States, France and Germany. Frequent co-authors include Ian Tenney, Dipanjan Das, Chris Callison-Burch, Tal Linzen, Benjamin Van Durme, Albert Webson, Quan Ze Chen, Wei Xu, Courtney Napoles and Tom Kwiatkowski. Their work appears in journals such as Proceedings of the National Academy of Sciences, Neuron and PLoS ONE.
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.