Daniela Gerz
Impact in
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Speech and dialogue systems
- Text Readability and Simplification
- Speech Recognition and Synthesis
- Advanced Text Analysis Techniques
- Text and Document Classification Technologies
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- Multimodal Machine Learning Applications
Papers in
-
- Topic Modeling 8
- Natural Language Processing Techniques 8
- Speech and dialogue systems 5
- Speech Recognition and Synthesis 3
- Co-authors
- Ivan Vulić (8 shared papers)Anna Korhonen (4 shared papers)Matthew Henderson (3 shared papers)Roi Reichart (2 shared papers)Edoardo Maria Ponti (2 shared papers)Pei-Hao Su (3 shared papers)Tsung-Hsien Wen (3 shared papers)Iñigo Casanueva (3 shared papers)
- Journals
- Computational Linguistics (1 paper)Transactions of the Association for Computational Linguistics (1 paper)Edinburgh Research Explorer (1 paper)Monash University Research Portal (Monash University) (1 paper)Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (1 paper)
- Partner nations
- United KingdomIsraelSouth Korea
In The Last Decade
Daniela Gerz
8 papers receiving 176 citations
Peers
Comparison fields: 5 of 28
- Artificial Intelligence 183
- Computer Vision and Pattern Recognition 32
- General Social Sciences 3
- Cultural Studies 6
- Information Systems 11
Countries citing papers authored by Daniela Gerz
This map shows the geographic impact of Daniela Gerz'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 Daniela Gerz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniela Gerz more than expected).
Fields of papers citing papers by Daniela Gerz
This network shows the impact of papers produced by Daniela Gerz. 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 Daniela Gerz. The network helps show where Daniela Gerz may publish in the future.
Co-authors
The 16 scholars most cited alongside Daniela Gerz, 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 | 43 | |
| 2 | 2017 | 37 | |
| 3 | 2018 | 28 | |
| 4 | 2020 | 27 | |
| 5 | 2018 | 27 | |
| 6 | 2021 | 19 | |
| 7 | 2019 | 8 | |
| 8 | 2019 | 6 |
About Daniela Gerz
Daniela Gerz is a scholar working on Artificial Intelligence, Infectious Diseases, Organic Chemistry, Surgery and Communication, having authored 8 papers that have together received 195 indexed citations. Recurring topics across this work include Topic Modeling (8 papers), Natural Language Processing Techniques (8 papers), Speech and dialogue systems (5 papers) and Speech Recognition and Synthesis (3 papers). The work is most often cited by research in Artificial Intelligence (183 citations), Computer Vision and Pattern Recognition (32 citations), General Social Sciences (3 citations), Cultural Studies (6 citations) and Information Systems (11 citations). Daniela Gerz has collaborated with scholars based in United Kingdom, Israel and South Korea. Frequent co-authors include Ivan Vulić, Anna Korhonen, Matthew Henderson, Roi Reichart, Edoardo Maria Ponti, Pei-Hao Su, Tsung-Hsien Wen, Iñigo Casanueva, Paweł Budzianowski and Douwe Kiela. Their work appears in journals such as Computational Linguistics, Transactions of the Association for Computational Linguistics, Edinburgh Research Explorer, Monash University Research Portal (Monash University) and Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.
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.