Felix Hieber
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition
- Information Systems
- Computer Science Applications
- Language and Linguistics
- Co-authors
- Stefan RiezlerTobias DomhanArtem SokolovChristina UngerPhilipp CimianoKe TranEva HaslerBill Byrne
- Topics
- Natural Language Processing Techniques (8 papers)Topic Modeling (8 papers)Semantic Web and Ontologies (3 papers)
- Cited by
- Artificial IntelligenceComputer Science ApplicationsComputer Vision and Pattern Recognition
- Journals
- RadiologyProceedings of the 2021 Conference on Empirical Methods in Natural Language ProcessingWorkshop on Statistical Machine Translation
- Partner nations
- GermanyUnited States
In The Last Decade
Felix Hieber
9 papers receiving 113 citations
Peers
Comparison fields: 5 of 18
- Artificial Intelligence 118
- Computer Vision and Pattern Recognition 31
- Information Systems 29
- Computer Science Applications 11
- Language and Linguistics 8
Countries citing papers authored by Felix Hieber
This map shows the geographic impact of Felix Hieber'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 Felix Hieber with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Felix Hieber more than expected).
Fields of papers citing papers by Felix Hieber
This network shows the impact of papers produced by Felix Hieber. 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 Felix Hieber. The network helps show where Felix Hieber may publish in the future.
Co-authorship network of co-authors of Felix Hieber
This figure shows the co-authorship network connecting the top 25 collaborators of Felix Hieber. A scholar is included among the top collaborators of Felix Hieber 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 Felix Hieber. Felix Hieber is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 38 | |
| 3 | 9 | |
| 4 | 6 | |
| 5 | 13 | |
| 6 | 8 | |
| 7 | Twitter Translation using Translation-Based Cross-Lingual Retrieval | 30 |
| 8 | 12 | |
| 9 | 5 |
About Felix Hieber
Felix Hieber is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems, having authored 9 papers that have together received 124 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (8 papers), Topic Modeling (8 papers) and Semantic Web and Ontologies (3 papers). The work is most often cited by research in Artificial Intelligence (118 citations), Computer Science Applications (11 citations) and Computer Vision and Pattern Recognition (31 citations). Felix Hieber has collaborated with scholars based in Germany and United States. Frequent co-authors include Stefan Riezler, Tobias Domhan, Artem Sokolov, Christina Unger, Philipp Cimiano, Ke Tran, Eva Hasler and Bill Byrne. Their work appears in journals such as Radiology, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing and Workshop on Statistical Machine Translation.
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.