Mark Cieliebak
About
In The Last Decade
Mark Cieliebak
53 papers receiving 531 citations
Peers
Comparison fields: 5 of 80
- Artificial Intelligence 298
- Computer Networks and Communications 114
- Molecular Biology 82
- Mechanical Engineering 72
- Spectroscopy 57
Countries citing papers authored by Mark Cieliebak
This map shows the geographic impact of Mark Cieliebak'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 Mark Cieliebak with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Cieliebak more than expected).
Fields of papers citing papers by Mark Cieliebak
This network shows the impact of papers produced by Mark Cieliebak. 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 Mark Cieliebak. The network helps show where Mark Cieliebak may publish in the future.
Co-authorship network of co-authors of Mark Cieliebak
This figure shows the co-authorship network connecting the top 25 collaborators of Mark Cieliebak. A scholar is included among the top collaborators of Mark Cieliebak 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 Mark Cieliebak. Mark Cieliebak is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 6 | |
| 5 | 1 | |
| 6 | Proceedings of the 4th edition of the Swiss Text Analytics Conference | 0 |
| 7 | SB-CH : a Swiss German corpus with sentiment annotations | 2 |
| 8 | spMMMP at GermEval 2018 shared task : classification of offensive content in tweets using convolutional neural networks and gated recurrent units | 2 |
| 9 | Word unigram weighing for author profiling at PAN 2018 : notebook for PAN at CLEF 2018 | 1 |
| 10 | Four different ways to build a chatbot about movies | 0 |
| 11 | Author Profiling with Bidirectional RNNs using Attention with GRUs. | 5 |
| 12 | Adverse drug reaction detection using an adapted sentiment classifier | 3 |
| 13 | Twitter can help to find adverse drug reactions | 1 |
| 14 | Flip your classroom : but be aware! | 1 |
| 15 | Meta-Classifiers Easily Improve Commercial Sentiment Detection Tools | 3 |
| 16 | Applied data science in Europe : challenges for academia in keeping up with a highly demanded topic | 6 |
| 17 | 10 | |
| 18 | 5 | |
| 19 | 7 | |
| 20 | Gathering Autonomous Mobile Robots. | 29 |
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.