Rotem Dror
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition
- Information Systems top 10%
- Molecular Biology
- Management Science and Operations Research
- Co-authors
- Roi ReichartSegev ShlomovDan RothDaniel DeutschMarina BogomolovGabriel StanovskyMoran MizrahiDafna Shahaf
- Topics
- Topic Modeling (12 papers)Natural Language Processing Techniques (11 papers)Advanced Text Analysis Techniques (3 papers)
- Journals
- Transactions of the Association for Computational LinguisticsProceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Partner nations
- IsraelUnited StatesGermany
In The Last Decade
Rotem Dror
13 papers receiving 389 citations
Peers
Comparison fields: 5 of 72
- Artificial Intelligence 366
- Computer Vision and Pattern Recognition 57
- Information Systems 51
- Molecular Biology 24
- Management Science and Operations Research 18
Countries citing papers authored by Rotem Dror
This map shows the geographic impact of Rotem Dror'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 Rotem Dror with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rotem Dror more than expected).
Fields of papers citing papers by Rotem Dror
This network shows the impact of papers produced by Rotem Dror. 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 Rotem Dror. The network helps show where Rotem Dror may publish in the future.
Co-authorship network of co-authors of Rotem Dror
This figure shows the co-authorship network connecting the top 25 collaborators of Rotem Dror. A scholar is included among the top collaborators of Rotem Dror 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 Rotem Dror. Rotem Dror 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 | 29 | |
| 3 | 10 | |
| 4 | 5 | |
| 5 | 6 | |
| 6 | 14 | |
| 7 | 16 | |
| 8 | 35 | |
| 9 | 8 | |
| 10 | 17 | |
| 11 | 56 | |
| 12 | 191 | |
| 13 | 38 |
About Rotem Dror
Rotem Dror is a scholar working on Artificial Intelligence, Information Systems and Management and Computer Vision and Pattern Recognition, having authored 13 papers that have together received 427 indexed citations. Recurring topics across this work include Topic Modeling (12 papers), Natural Language Processing Techniques (11 papers) and Advanced Text Analysis Techniques (3 papers). The work is most often cited by research in Artificial Intelligence (366 citations), Health Informatics (8 citations) and Computer Vision and Pattern Recognition (57 citations). Rotem Dror has collaborated with scholars based in Israel, United States and Germany. Frequent co-authors include Roi Reichart, Segev Shlomov, Dan Roth, Daniel Deutsch, Marina Bogomolov, Gabriel Stanovsky, Moran Mizrahi, Dafna Shahaf, Haoyu Wang and Steffen Eger. Their work appears in journals such as Transactions of the Association for Computational Linguistics and Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.
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.