Daniel Simig
Impact in
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Domain Adaptation and Few-Shot Learning
- Text and Document Classification Technologies
- Sentiment Analysis and Opinion Mining
Papers in
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- Topic Modeling 6
- Domain Adaptation and Few-Shot Learning 2
- Text Readability and Simplification 1
- Text and Document Classification Technologies 1
- Natural Language Processing Techniques 1
- Algorithms and Data Compression 1
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- Time Series Analysis and Forecasting 1
- Co-authors
- Armen Aghajanyan (2 shared papers)Luke Zettlemoyer (1 shared paper)Kushal Tirumala (1 shared paper)Kashyap Popat (1 shared paper)Majid Yazdani (1 shared paper)Tianlu Wang (2 shared papers)Sebastian Riedel (1 shared paper)Todor Mihaylov (1 shared paper)
- Journals
- neural information processing systems (2 papers)Findings of the Association for Computational Linguistics: ACL 2022 (1 paper)Annual Meeting of the Association for Computational Linguistics (ACL) (1 paper)Conference on Empirical Methods in Natural Language Processing (EMNLP) (1 paper)
- Partner nations
- United StatesMongoliaUnited Kingdom
In The Last Decade
Daniel Simig
6 papers receiving 112 citations
Peers
Comparison fields: 5 of 26
- Artificial Intelligence 90
- General Social Sciences 2
- Health Informatics 1
- Safety Research 5
- Software 2
Countries citing papers authored by Daniel Simig
This map shows the geographic impact of Daniel Simig'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 Daniel Simig with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Simig more than expected).
Fields of papers citing papers by Daniel Simig
This network shows the impact of papers produced by Daniel Simig. 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 Daniel Simig. The network helps show where Daniel Simig may publish in the future.
Co-authors
The 17 scholars most cited alongside Daniel Simig, 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 | 2022 | 80 | |
| 2 | 2023 | 12 | |
| 3 | 2023 | 8 | |
| 4 | 2022 | 7 | |
| 5 | 2023 | 7 | |
| 6 | 2022 | 1 |
About Daniel Simig
Daniel Simig is a scholar working on Artificial Intelligence, Signal Processing, Nuclear Energy and Engineering, Waste Management and Disposal and Infectious Diseases, having authored 6 papers that have together received 115 indexed citations. Recurring topics across this work include Topic Modeling (6 papers), Domain Adaptation and Few-Shot Learning (2 papers), Text Readability and Simplification (1 paper), Text and Document Classification Technologies (1 paper), Natural Language Processing Techniques (1 paper), Algorithms and Data Compression (1 paper) and Time Series Analysis and Forecasting (1 paper). The work is most often cited by research in Artificial Intelligence (90 citations), General Social Sciences (2 citations), Health Informatics (1 citation), Safety Research (5 citations) and Software (2 citations). Daniel Simig has collaborated with scholars based in United States, Mongolia and United Kingdom. Frequent co-authors include Armen Aghajanyan, Luke Zettlemoyer, Kushal Tirumala, Kashyap Popat, Majid Yazdani, Tianlu Wang, Sebastian Riedel, Todor Mihaylov, Xiaochuang Han and Yulia Tsvetkov. Their work appears in journals such as neural information processing systems, Findings of the Association for Computational Linguistics: ACL 2022, Annual Meeting of the Association for Computational Linguistics (ACL) and Conference on Empirical Methods in Natural Language Processing (EMNLP).
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.