Sascha Rothe
Impact in
- Artificial Intelligence top 5%
- Topic Modeling
- Natural Language Processing Techniques
- Text Readability and Simplification
- Advanced Graph Neural Networks
- Advanced Text Analysis Techniques
- Speech and dialogue systems
- Information Systems top 10%
- Information Retrieval and Search Behavior
Papers in
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- Topic Modeling 9
- Natural Language Processing Techniques 7
- Text Readability and Simplification 3
- Advanced Graph Neural Networks 2
- Sentiment Analysis and Opinion Mining 1
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- Handwritten Text Recognition Techniques 1
- Co-authors
- Hinrich Schütze (3 shared papers)Enrique Alfonseca (1 shared paper)Mostafa Dehghani (1 shared paper)Eric Malmi (1 shared paper)Sebastian Krause (1 shared paper)Daniil Mirylenka (1 shared paper)Aliaksei Severyn (1 shared paper)Katja Filippova (1 shared paper)
- Journals
- Computational Linguistics (1 paper)Electronic Theses of LMU Munich (Ludwig-Maximilians-Universität München) (1 paper)Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (1 paper)UvA-DARE (University of Amsterdam) (1 paper)
- Partner nations
- GermanySwitzerlandUnited States
In The Last Decade
Sascha Rothe
9 papers receiving 241 citations
Peers
Comparison fields: 5 of 34
- Artificial Intelligence 236
- Information Systems 58
- Computer Vision and Pattern Recognition 33
- Statistical and Nonlinear Physics 11
- Health Informatics 1
Countries citing papers authored by Sascha Rothe
This map shows the geographic impact of Sascha Rothe'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 Sascha Rothe with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sascha Rothe more than expected).
Fields of papers citing papers by Sascha Rothe
This network shows the impact of papers produced by Sascha Rothe. 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 Sascha Rothe. The network helps show where Sascha Rothe may publish in the future.
Co-authors
The 12 scholars most cited alongside Sascha Rothe, 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 | 2019 | 79 | |
| 2 | 2017 | 71 | |
| 3 | 2018 | 30 | |
| 4 | 2016 | 22 | |
| 5 | 2014 | 20 | |
| 6 | 2021 | 17 | |
| 7 | 2017 | 10 | |
| 8 | 2023 | 10 | |
| 9 | 2017 | 1 |
About Sascha Rothe
Sascha Rothe is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Statistical and Nonlinear Physics and Infectious Diseases, having authored 9 papers that have together received 260 indexed citations. Recurring topics across this work include Topic Modeling (9 papers), Natural Language Processing Techniques (7 papers), Text Readability and Simplification (3 papers), Advanced Graph Neural Networks (2 papers), Handwritten Text Recognition Techniques (1 paper), Complex Network Analysis Techniques (1 paper), Web Data Mining and Analysis (1 paper) and Sentiment Analysis and Opinion Mining (1 paper). The work is most often cited by research in Artificial Intelligence (236 citations), Information Systems (58 citations), Computer Vision and Pattern Recognition (33 citations), Statistical and Nonlinear Physics (11 citations) and Health Informatics (1 citation). Sascha Rothe has collaborated with scholars based in Germany, Switzerland and United States. Frequent co-authors include Hinrich Schütze, Enrique Alfonseca, Mostafa Dehghani, Eric Malmi, Sebastian Krause, Daniil Mirylenka, Aliaksei Severyn, Katja Filippova, Katharina Kann and Shashi Narayan. Their work appears in journals such as Computational Linguistics, Electronic Theses of LMU Munich (Ludwig-Maximilians-Universität München), Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing and UvA-DARE (University of Amsterdam).
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.