Jonathan Bischof
Impact in
- General Social Sciences top 0.5%
- Computational and Text Analysis Methods
- Health Informatics top 10%
Papers in
-
- Natural Language Processing Techniques 3
- Topic Modeling 3
- Advanced Text Analysis Techniques 1
-
- Computational and Text Analysis Methods 2
- Co-authors
- Edoardo M. Airoldi (4 shared papers)Jilin Chen (1 shared paper)Alex Beutel (1 shared paper)Allison Woodruff (1 shared paper)Hai Qian (1 shared paper)Tulsee Doshi (1 shared paper)Ed H. (1 shared paper)
- Journals
- Journal of the American Statistical Association (1 paper)arXiv (Cornell University) (1 paper)International Conference on Machine Learning (1 paper)
- Partner nations
- United States
In The Last Decade
Jonathan Bischof
6 papers receiving 269 citations
Peers
Comparison fields: 5 of 71
- General Social Sciences 76
- Health Informatics 12
- Safety Research 36
- Communication 30
- Artificial Intelligence 94
Countries citing papers authored by Jonathan Bischof
This map shows the geographic impact of Jonathan Bischof'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 Jonathan Bischof with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonathan Bischof more than expected).
Fields of papers citing papers by Jonathan Bischof
This network shows the impact of papers produced by Jonathan Bischof. 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 Jonathan Bischof. The network helps show where Jonathan Bischof may publish in the future.
Co-authors
The 7 scholars most cited alongside Jonathan Bischof, 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 | 2015 | 105 | |
| 2 | Summarizing topical content with word frequency and exclusivity | 2012 | 102 |
| 3 | 2019 | 62 | |
| 4 | Capturing topical content with frequency and exclusivity. | 2012 | 3 |
| 5 | A Bootstrap Approach to Time Invariance in Panel Data | 2009 | 3 |
| 6 | Poisson convolution on a tree of categories for modeling topical content with word frequency and exclusivity | 2012 | 1 |
About Jonathan Bischof
Jonathan Bischof is a scholar working on Artificial Intelligence, General Social Sciences, Molecular Biology, Sociology and Political Science and Computer Vision and Pattern Recognition, having authored 6 papers that have together received 276 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (3 papers), Topic Modeling (3 papers), Computational and Text Analysis Methods (2 papers), Video Analysis and Summarization (1 paper), Qualitative Comparative Analysis Research (1 paper), Biomedical Text Mining and Ontologies (1 paper), Complex Network Analysis Techniques (1 paper) and Advanced Text Analysis Techniques (1 paper). The work is most often cited by research in General Social Sciences (76 citations), Health Informatics (12 citations), Safety Research (36 citations), Communication (30 citations) and Artificial Intelligence (94 citations). Jonathan Bischof has collaborated with scholars based in United States. Frequent co-authors include Edoardo M. Airoldi, Jilin Chen, Alex Beutel, Allison Woodruff, Hai Qian, Tulsee Doshi and Ed H.. Their work appears in journals such as Journal of the American Statistical Association, arXiv (Cornell University) and International Conference on Machine Learning.
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.