Evelyne Viegas
- Artificial Intelligence top 5%
- Information Systems top 10%
- Experimental and Cognitive Psychology
- Computer Vision and Pattern Recognition
- Language and Linguistics top 10%
- Co-authors
- ChengXiang ZhaiKavita GanesanXiaolong LiBo-June HsuKuansan WangSérgio EscaleraHugo Jair EscalanteIsabelle Guyon
- Topics
- Natural Language Processing Techniques (17 papers)Semantic Web and Ontologies (7 papers)Topic Modeling (6 papers)
- Journals
- Communications of the ACMIEEE Transactions on Affective ComputingLanguage Resources and Evaluation
- Partner nations
- United StatesUnited KingdomMexico
In The Last Decade
Evelyne Viegas
28 papers receiving 406 citations
Peers
Comparison fields: 5 of 83
- Artificial Intelligence 293
- Information Systems 90
- Experimental and Cognitive Psychology 52
- Computer Vision and Pattern Recognition 44
- Language and Linguistics 35
Countries citing papers authored by Evelyne Viegas
This map shows the geographic impact of Evelyne Viegas'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 Evelyne Viegas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Evelyne Viegas more than expected).
Fields of papers citing papers by Evelyne Viegas
This network shows the impact of papers produced by Evelyne Viegas. 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 Evelyne Viegas. The network helps show where Evelyne Viegas may publish in the future.
Co-authorship network of co-authors of Evelyne Viegas
This figure shows the co-authorship network connecting the top 25 collaborators of Evelyne Viegas. A scholar is included among the top collaborators of Evelyne Viegas based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Evelyne Viegas. Evelyne Viegas is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 63 | |
| 2 | 1 | |
| 3 | 12 | |
| 4 | 32 | |
| 5 | A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention | 28 |
| 6 | Automatic Machine Learning (AutoML) | 4 |
| 7 | 59 | |
| 8 | An Overview of Microsoft Web N-gram Corpus and Applications | 73 |
| 9 | 4 | |
| 10 | 4 | |
| 11 | 8 | |
| 12 | 5 | |
| 13 | Extending a core Lexicon using on- line language resources with savoir-faire | 2 |
| 14 | 2 | |
| 15 | 1 | |
| 16 | Intelligent Planning Meets Intelligent Planners | 1 |
| 17 | Two principles and six techniques for rapid MT development | 4 |
| 18 | 2 | |
| 19 | The Ecology of Lexical Acquisition: Computational Lexicon Making Process | 2 |
| 20 | A Semi-Polymorphic Approach to the Interpretation of Adjectival Constructions: A Cross-Linguistic Perspective | 4 |
About Evelyne Viegas
Evelyne Viegas is a scholar working on Artificial Intelligence, Language and Linguistics and Computer Science Applications, having authored 32 papers that have together received 450 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (17 papers), Semantic Web and Ontologies (7 papers) and Topic Modeling (6 papers). The work is most often cited by research in Artificial Intelligence (293 citations), Information Systems (90 citations) and Experimental and Cognitive Psychology (52 citations). Evelyne Viegas has collaborated with scholars based in United States, United Kingdom and Mexico. Frequent co-authors include ChengXiang Zhai, Kavita Ganesan, Xiaolong Li, Bo-June Hsu, Kuansan Wang, Sérgio Escalera, Hugo Jair Escalante, Isabelle Guyon, Pierrette Bouillon and Núria Macià. Their work appears in journals such as Communications of the ACM, IEEE Transactions on Affective Computing and Language Resources and Evaluation.
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.