Eva Schlinger
Impact in
- Artificial Intelligence top 2%
- Topic Modeling
- Natural Language Processing Techniques
- Sentiment Analysis and Opinion Mining
- Hate Speech and Cyberbullying Detection
- Text Readability and Simplification
- Advanced Text Analysis Techniques
- Speech Recognition and Synthesis
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- Multimodal Machine Learning Applications
Papers in
-
- Topic Modeling 6
- Natural Language Processing Techniques 6
- Text Readability and Simplification 2
- Speech and dialogue systems 1
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- Multimodal Machine Learning Applications 1
- Handwritten Text Recognition Techniques 1
- Co-authors
- Dan Garrette (1 shared paper)Telmo Pires (1 shared paper)Chris Dyer (4 shared papers)Victor Chahuneau (2 shared papers)William W. Cohen (1 shared paper)Ming‐Wei Chang (1 shared paper)Noah A. Smith (1 shared paper)William Yang Wang (1 shared paper)
- Journals
- The Prague Bulletin of Mathematical Linguistics (1 paper)Figshare (1 paper)International Conference on Learning Representations (1 paper)
- Partner nations
- United States
In The Last Decade
Eva Schlinger
5 papers receiving 867 citations
Eva Schlinger's Hit Papers
Peers
Comparison fields: 5 of 58
- Artificial Intelligence 844
- Computer Vision and Pattern Recognition 190
- General Social Sciences 11
- Information Systems 75
- Health Informatics 4
Countries citing papers authored by Eva Schlinger
This map shows the geographic impact of Eva Schlinger'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 Eva Schlinger with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eva Schlinger more than expected).
Fields of papers citing papers by Eva Schlinger
This network shows the impact of papers produced by Eva Schlinger. 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 Eva Schlinger. The network helps show where Eva Schlinger may publish in the future.
Co-authors
The 15 scholars most cited alongside Eva Schlinger, 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 | How Multilingual is Multilingual BERT? Hit paper breakdown → | 2019 | 811 |
| 2 | Open Question Answering over Tables and Text | 2021 | 54 |
| 3 | 2013 | 43 | |
| 4 | 2014 | 11 | |
| 5 | 2013 | 3 | |
| 6 | 2016 | 1 |
About Eva Schlinger
Eva Schlinger is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Language and Linguistics, Insect Science and Renewable Energy, Sustainability and the Environment, having authored 6 papers that have together received 923 indexed citations. Recurring topics across this work include Topic Modeling (6 papers), Natural Language Processing Techniques (6 papers), Text Readability and Simplification (2 papers), Translation Studies and Practices (1 paper), Multimodal Machine Learning Applications (1 paper), Speech and dialogue systems (1 paper) and Handwritten Text Recognition Techniques (1 paper). The work is most often cited by research in Artificial Intelligence (844 citations), Computer Vision and Pattern Recognition (190 citations), General Social Sciences (11 citations), Information Systems (75 citations) and Health Informatics (4 citations). Eva Schlinger has collaborated with scholars based in United States. Frequent co-authors include Dan Garrette, Telmo Pires, Chris Dyer, Victor Chahuneau, William W. Cohen, Ming‐Wei Chang, Noah A. Smith, William Yang Wang, Wen-Hu Chen and Archna Bhatia. Their work appears in journals such as The Prague Bulletin of Mathematical Linguistics, Figshare and International Conference on Learning Representations.
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.