Eva Schlinger
Impact in
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Speech and dialogue systems
- Text Readability and Simplification
- Advanced Text Analysis Techniques
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- Multimodal Machine Learning Applications
- Handwritten Text Recognition Techniques
Papers in
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- Topic Modeling 5
- Natural Language Processing Techniques 5
- Speech and dialogue systems 1
- Text Readability and Simplification 1
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- Handwritten Text Recognition Techniques 1
- Multimodal Machine Learning Applications 1
- Co-authors
- Chris Dyer (4 shared papers)Victor Chahuneau (2 shared papers)William Yang Wang (1 shared paper)Noah A. Smith (1 shared paper)Ming‐Wei Chang (1 shared paper)William W. Cohen (1 shared paper)Wenhu Chen (1 shared paper)Waleed Ammar (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
4 papers receiving 97 citations
Peers
Comparison fields: 5 of 11
- Artificial Intelligence 104
- Computer Vision and Pattern Recognition 32
- Management Science and Operations Research 8
- Language and Linguistics 6
- Information Systems 6
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 13 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 | Open Question Answering over Tables and Text | 2021 | 51 |
| 2 | 2013 | 41 | |
| 3 | 2014 | 10 | |
| 4 | 2013 | 3 | |
| 5 | 2016 | 1 |
About Eva Schlinger
Eva Schlinger is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Language and Linguistics, Infectious Diseases and Organic Chemistry, having authored 5 papers that have together received 106 indexed citations. Recurring topics across this work include Topic Modeling (5 papers), Natural Language Processing Techniques (5 papers), Speech and dialogue systems (1 paper), Text Readability and Simplification (1 paper), Handwritten Text Recognition Techniques (1 paper), Translation Studies and Practices (1 paper) and Multimodal Machine Learning Applications (1 paper). The work is most often cited by research in Artificial Intelligence (104 citations), Computer Vision and Pattern Recognition (32 citations), Management Science and Operations Research (8 citations), Language and Linguistics (6 citations) and Information Systems (6 citations). Eva Schlinger has collaborated with scholars based in United States. Frequent co-authors include Chris Dyer, Victor Chahuneau, William Yang Wang, Noah A. Smith, Ming‐Wei Chang, William W. Cohen, Wenhu Chen, Waleed Ammar, Archna Bhatia and Alon Lavie. 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.