Andrew Drozdov
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Information Systems
- Cognitive Neuroscience
- Signal Processing
- Co-authors
- Adina WilliamsSamuel R. BowmanAndrew McCallumMohit IyyerPatrick VergaMohit YadavTim O’GormanKyunghyun Cho
- Topics
- Natural Language Processing Techniques (13 papers)Topic Modeling (12 papers)Speech Recognition and Synthesis (4 papers)
- Journals
- Transactions of the Association for Computational LinguisticsarXiv (Cornell University)Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Partner nations
- United States
In The Last Decade
Andrew Drozdov
11 papers receiving 143 citations
Peers
Comparison fields: 5 of 24
- Artificial Intelligence 143
- Computer Vision and Pattern Recognition 61
- Information Systems 5
- Cognitive Neuroscience 5
- Signal Processing 4
Countries citing papers authored by Andrew Drozdov
This map shows the geographic impact of Andrew Drozdov'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 Andrew Drozdov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrew Drozdov more than expected).
Fields of papers citing papers by Andrew Drozdov
This network shows the impact of papers produced by Andrew Drozdov. 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 Andrew Drozdov. The network helps show where Andrew Drozdov may publish in the future.
Co-authorship network of co-authors of Andrew Drozdov
This figure shows the co-authorship network connecting the top 25 collaborators of Andrew Drozdov. A scholar is included among the top collaborators of Andrew Drozdov 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 Andrew Drozdov. Andrew Drozdov is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 4 | |
| 5 | 4 | |
| 6 | 6 | |
| 7 | 3 | |
| 8 | 14 | |
| 9 | 6 | |
| 10 | 54 | |
| 11 | 42 | |
| 12 | Learning to parse from a semantic objective: It works. Is it syntax? | 8 |
| 13 | Emergent Language in a Multi-Modal, Multi-Step Referential Game. | 6 |
About Andrew Drozdov
Andrew Drozdov is a scholar working on Artificial Intelligence, Cultural Studies and Computer Vision and Pattern Recognition, having authored 13 papers that have together received 151 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (13 papers), Topic Modeling (12 papers) and Speech Recognition and Synthesis (4 papers). The work is most often cited by research in Artificial Intelligence (143 citations), Computer Vision and Pattern Recognition (61 citations) and Health Informatics (1 citation). Andrew Drozdov has collaborated with scholars based in United States. Frequent co-authors include Adina Williams, Samuel R. Bowman, Andrew McCallum, Mohit Iyyer, Patrick Verga, Mohit Yadav, Tim O’Gorman, Kyunghyun Cho, Douwe Kiela and Hamed Zamani. Their work appears in journals such as Transactions of the Association for Computational Linguistics, arXiv (Cornell University) and Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.
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.