Daniel Andor
Impact in
- Artificial Intelligence top 2%
- Natural Language Processing Techniques
- Topic Modeling
- Adversarial Robustness in Machine Learning
- Sentiment Analysis and Opinion Mining
- Speech and dialogue systems
- Domain Adaptation and Few-Shot Learning
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- Multimodal Machine Learning Applications
Papers in
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- Topic Modeling 5
- Natural Language Processing Techniques 5
- Explainable Artificial Intelligence (XAI) 1
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- Information Retrieval and Search Behavior 1
- Co-authors
- Chris Alberti (3 shared papers)Michael Collins (3 shared papers)Emma Strubell (1 shared paper)Andrew McCallum (1 shared paper)Patrick W. Verga (1 shared paper)Emily Pitler (4 shared papers)Slav Petrov (1 shared paper)Kuzman Ganchev (1 shared paper)
- Journals
- Transactions of the Association for Computational Linguistics (1 paper)Communications in computer and information science (1 paper)Annual Meeting of the Association for Computational Linguistics (ACL) (3 papers)Conference on Empirical Methods in Natural Language Processing (EMNLP) (2 papers)
- Partner nations
- United StatesPortugal
In The Last Decade
Daniel Andor
8 papers receiving 866 citations
Daniel Andor's Hit Papers
Peers
Comparison fields: 5 of 37
- Artificial Intelligence 822
- Computer Vision and Pattern Recognition 50
- Information Systems 46
- Language and Linguistics 16
- Software 4
Countries citing papers authored by Daniel Andor
This map shows the geographic impact of Daniel Andor'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 Andor with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Andor more than expected).
Fields of papers citing papers by Daniel Andor
This network shows the impact of papers produced by Daniel Andor. 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 Andor. The network helps show where Daniel Andor may publish in the future.
Co-authors
The 21 scholars most cited alongside Daniel Andor, 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 | Globally Normalized Transition-Based Neural Networks Hit paper breakdown → | 2016 | 335 |
| 2 | 2018 | 286 | |
| 3 | 2019 | 148 | |
| 4 | 2019 | 55 | |
| 5 | 2018 | 51 | |
| 6 | 2021 | 27 | |
| 7 | 2018 | 11 | |
| 8 | 2020 | 5 |
About Daniel Andor
Daniel Andor is a scholar working on Artificial Intelligence, Information Systems, Molecular Biology, Nuclear Energy and Engineering and Waste Management and Disposal, having authored 8 papers that have together received 918 indexed citations. Recurring topics across this work include Topic Modeling (5 papers), Natural Language Processing Techniques (5 papers), Information Retrieval and Search Behavior (1 paper), Biomedical Text Mining and Ontologies (1 paper) and Explainable Artificial Intelligence (XAI) (1 paper). The work is most often cited by research in Artificial Intelligence (822 citations), Computer Vision and Pattern Recognition (50 citations), Information Systems (46 citations), Language and Linguistics (16 citations) and Software (4 citations). Daniel Andor has collaborated with scholars based in United States and Portugal. Frequent co-authors include Chris Alberti, Michael Collins, Emma Strubell, Andrew McCallum, Patrick W. Verga, Emily Pitler, Slav Petrov, Kuzman Ganchev, Aliaksei Severyn and Jacob Devlin. Their work appears in journals such as Transactions of the Association for Computational Linguistics, Communications in computer and information science, 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.