Irina Matveeva
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition
- Molecular Biology
- Computational Theory and Mathematics
- Information Systems
- Co-authors
- Hervé DéjeanCyril GoutteÉric GaussierGina‐Anne LevowYu HuJohn GoldsmithAhmed H. YousefMona Diab
- Topics
- Topic Modeling (7 papers)Natural Language Processing Techniques (6 papers)Semantic Web and Ontologies (3 papers)
- Cited by
- Artificial IntelligenceComputer Vision and Pattern RecognitionComputational Theory and Mathematics
- Journals
- Empirical Methods in Natural Language ProcessingInternational Conference on Computational Linguistics
- Partner nations
- United StatesSwitzerlandRussia
In The Last Decade
Irina Matveeva
10 papers receiving 92 citations
Peers
Comparison fields: 5 of 18
- Artificial Intelligence 96
- Computer Vision and Pattern Recognition 12
- Molecular Biology 11
- Computational Theory and Mathematics 8
- Information Systems 5
Countries citing papers authored by Irina Matveeva
This map shows the geographic impact of Irina Matveeva'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 Irina Matveeva with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Irina Matveeva more than expected).
Fields of papers citing papers by Irina Matveeva
This network shows the impact of papers produced by Irina Matveeva. 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 Irina Matveeva. The network helps show where Irina Matveeva may publish in the future.
Co-authorship network of co-authors of Irina Matveeva
This figure shows the co-authorship network connecting the top 25 collaborators of Irina Matveeva. A scholar is included among the top collaborators of Irina Matveeva 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 Irina Matveeva. Irina Matveeva is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 4 | |
| 5 | Workshop Proceedings of TextGraphs-7: Graph-based Methods for Natural Language Processing | 7 |
| 6 | Coling 2008: Proceedings of the 3rd Textgraphs workshop on Graph-based Algorithms for Natural Language Processing | 1 |
| 7 | Proceedings of the 3rd Textgraphs Workshop on Graph-Based Algorithms for Natural Language Processing | 4 |
| 8 | Topic Segmentation with Hybrid Document Indexing | 6 |
| 9 | 1 | |
| 10 | 13 | |
| 11 | 65 |
About Irina Matveeva
Irina Matveeva is a scholar working on Artificial Intelligence, Cultural Studies and Computer Vision and Pattern Recognition, having authored 11 papers that have together received 103 indexed citations. Recurring topics across this work include Topic Modeling (7 papers), Natural Language Processing Techniques (6 papers) and Semantic Web and Ontologies (3 papers). The work is most often cited by research in Artificial Intelligence (96 citations), Computer Vision and Pattern Recognition (12 citations) and Computational Theory and Mathematics (8 citations). Irina Matveeva has collaborated with scholars based in United States, Switzerland and Russia. Frequent co-authors include Hervé Déjean, Cyril Goutte, Éric Gaussier, Gina‐Anne Levow, Yu Hu, John Goldsmith, Ahmed H. Yousef, Mona Diab, Chris Biemann and Monojit Choudhury. Their work appears in journals such as Empirical Methods in Natural Language Processing and International Conference on Computational Linguistics.
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.