Stefan Schweter
Impact in
- Artificial Intelligence top 5%
- Topic Modeling
- Natural Language Processing Techniques
- Sentiment Analysis and Opinion Mining
- Text Readability and Simplification
- Advanced Text Analysis Techniques
- Text and Document Classification Technologies
- Hate Speech and Cyberbullying Detection
Papers in
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- Natural Language Processing Techniques 4
- Topic Modeling 4
- Text Readability and Simplification 3
- Authorship Attribution and Profiling 1
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- Libraries and Information Services 1
- Co-authors
- Kashif Rasul (1 shared paper)Duncan A. J. Blythe (1 shared paper)Alan Akbik (1 shared paper)Roland Vollgraf (1 shared paper)
- Journals
- Zeitschrift für Bibliothekswesen und Bibliographie (1 paper)Zenodo (CERN European Organization for Nuclear Research) (1 paper)CLEF (Working Notes) (1 paper)
In The Last Decade
Stefan Schweter
5 papers receiving 327 citations
Peers
Comparison fields: 5 of 60
- Artificial Intelligence 312
- Computational Mathematics 2
- Information Systems 44
- Issues, ethics and legal aspects 2
- General Social Sciences 5
Countries citing papers authored by Stefan Schweter
This map shows the geographic impact of Stefan Schweter'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 Stefan Schweter with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stefan Schweter more than expected).
Fields of papers citing papers by Stefan Schweter
This network shows the impact of papers produced by Stefan Schweter. 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 Stefan Schweter. The network helps show where Stefan Schweter may publish in the future.
Co-authors
The 4 scholars most cited alongside Stefan Schweter, 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 | 179 | |
| 2 | 2020 | 93 | |
| 3 | 2020 | 80 | |
| 4 | Deep-EOS: General-Purpose Neural Networks for Sentence Boundary Detection. | 2019 | 4 |
| 5 | Triple E - Effective Ensembling of Embeddings and Language Models for NER of Historical German. | 2020 | 2 |
| 6 | 2022 | 1 |
About Stefan Schweter
Stefan Schweter is a scholar working on Artificial Intelligence, Museology, History, Visual Arts and Performing Arts and Infectious Diseases, having authored 6 papers that have together received 359 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (4 papers), Topic Modeling (4 papers), Text Readability and Simplification (3 papers), Libraries and Information Services (1 paper), Visual Culture and Art Theory (1 paper), Authorship Attribution and Profiling (1 paper) and Historical and Religious Studies of Rome (1 paper). The work is most often cited by research in Artificial Intelligence (312 citations), Computational Mathematics (2 citations), Information Systems (44 citations), Issues, ethics and legal aspects (2 citations) and General Social Sciences (5 citations). Frequent co-authors include Kashif Rasul, Duncan A. J. Blythe, Alan Akbik and Roland Vollgraf. Their work appears in journals such as Zeitschrift für Bibliothekswesen und Bibliographie, Zenodo (CERN European Organization for Nuclear Research) and CLEF (Working Notes).
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.