Daniel Bär
Impact in
- Artificial Intelligence top 5%
- Topic Modeling
- Natural Language Processing Techniques
- Advanced Text Analysis Techniques
- Semantic Web and Ontologies
- Text Readability and Simplification
- Text and Document Classification Technologies
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- Multimodal Machine Learning Applications
Papers in
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- Topic Modeling 6
- Advanced Text Analysis Techniques 4
- Natural Language Processing Techniques 4
- Authorship Attribution and Profiling 2
- Semantic Web and Ontologies 1
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- Advanced Thermodynamics and Statistical Mechanics 2
- Co-authors
- Torsten Zesch (7 shared papers)Iryna Gurevych (7 shared papers)Chris Biemann (1 shared paper)
- Journals
- International Journal of Theoretical Physics (1 paper)Ingénierie des systèmes d information (1 paper)NeuroQuantology (1 paper)TUbilio (Technical University of Darmstadt) (6 papers)
In The Last Decade
Daniel Bär
7 papers receiving 208 citations
Peers
Comparison fields: 5 of 36
- Artificial Intelligence 225
- Computer Vision and Pattern Recognition 21
- Information Systems 20
- Communication 6
- General Social Sciences 2
Countries citing papers authored by Daniel Bär
This map shows the geographic impact of Daniel Bär'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 Daniel Bär with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Bär more than expected).
Fields of papers citing papers by Daniel Bär
This network shows the impact of papers produced by Daniel Bär. 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 Daniel Bär. The network helps show where Daniel Bär may publish in the future.
Co-authors
The 3 scholars most cited alongside Daniel Bär, 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 | 2012 | 139 | |
| 2 | 2013 | 43 | |
| 3 | 2012 | 31 | |
| 4 | 2011 | 16 | |
| 5 | 2015 | 9 | |
| 6 | Wikulu: An Extensible Architecture for Integrating Natural Language Processing Techniques with Wikis | 2011 | 2 |
| 7 | 2003 | 1 | |
| 8 | 2009 | 1 | |
| 9 | First Aid for Information Chaos in Wikis - Collaborative Information Management Enhanced Through Language Technology. | 2011 | 0 |
About Daniel Bär
Daniel Bär is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics, Atomic and Molecular Physics, and Optics, Communication and Molecular Biology, having authored 9 papers that have together received 242 indexed citations. Recurring topics across this work include Topic Modeling (6 papers), Advanced Text Analysis Techniques (4 papers), Natural Language Processing Techniques (4 papers), Authorship Attribution and Profiling (2 papers), Advanced Thermodynamics and Statistical Mechanics (2 papers), Quantum Mechanics and Applications (2 papers), Semantic Web and Ontologies (1 paper) and Cosmology and Gravitation Theories (1 paper). The work is most often cited by research in Artificial Intelligence (225 citations), Computer Vision and Pattern Recognition (21 citations), Information Systems (20 citations), Communication (6 citations) and General Social Sciences (2 citations). Daniel Bär has collaborated with scholars based in Germany and Israel. Frequent co-authors include Torsten Zesch, Iryna Gurevych and Chris Biemann. Their work appears in journals such as International Journal of Theoretical Physics, Ingénierie des systèmes d information, NeuroQuantology and TUbilio (Technical University of Darmstadt).
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.