György Szarvas
- Artificial Intelligence top 1%
- Molecular Biology
- Computer Vision and Pattern Recognition top 5%
- Health Information Management top 1%
- Information Systems top 10%
- Co-authors
- Richárd FarkasVeronika VinczeJános CsirikGyörgy MóraRóbert Busa‐FeketeIryna GurevychMarcus RohrbachMichael Stark
- Topics
- Topic Modeling (21 papers)Natural Language Processing Techniques (13 papers)Biomedical Text Mining and Ontologies (12 papers)
In The Last Decade
György Szarvas
29 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 79
- Artificial Intelligence 1.1k
- Molecular Biology 532
- Computer Vision and Pattern Recognition 155
- Health Information Management 120
- Information Systems 89
Countries citing papers authored by György Szarvas
This map shows the geographic impact of György Szarvas'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 György Szarvas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites György Szarvas more than expected).
Fields of papers citing papers by György Szarvas
This network shows the impact of papers produced by György Szarvas. 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 György Szarvas. The network helps show where György Szarvas may publish in the future.
Co-authorship network of co-authors of György Szarvas
This figure shows the co-authorship network connecting the top 25 collaborators of György Szarvas. A scholar is included among the top collaborators of György Szarvas 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 György Szarvas. György Szarvas 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 | 2 | |
| 3 | 13 | |
| 4 | Supervised All-Words Lexical Substitution using Delexicalized Features | 24 |
| 5 | An apple-to-apple comparison of Learning-to-rank algorithms in terms of Normalized Discounted Cumulative Gain | 10 |
| 6 | 15 | |
| 7 | TUD: Semantic Relatedness for Relation Classification | 1 |
| 8 | Proceedings of the Fourteenth Conference on Computational Natural Language Learning --- Shared Task | 16 |
| 9 | Linguistic scope-based and biological event-based speculation and negation annotations in the Genia Event and Bio-Scope corpora | 4 |
| 10 | The CoNLL-2010 Shared Task: Learning to Detect Hedges and their Scope in Natural Language Text | 173 |
| 11 | 21 | |
| 12 | Hedge Classification in Biomedical Texts with a Weakly Supervised Selection of Keywords | 56 |
| 13 | Hungarian Word-Sense Disambiguated Corpus. | 3 |
| 14 | 113 | |
| 15 | 282 | |
| 16 | 67 | |
| 17 | 94 | |
| 18 | Automatic extraction of semantic content from medical discharge records | 11 |
| 19 | A highly accurate Named Entity corpus for Hungarian | 14 |
| 20 | Named entity recognition for Hungarian using various machine learning algorithms | 4 |
About György Szarvas
György Szarvas is a scholar working on Artificial Intelligence, General Social Sciences and Health Information Management, having authored 31 papers that have together received 1.3k indexed citations. Recurring topics across this work include Topic Modeling (21 papers), Natural Language Processing Techniques (13 papers) and Biomedical Text Mining and Ontologies (12 papers). The work is most often cited by research in Artificial Intelligence (1.1k citations), Health Information Management (120 citations) and Molecular Biology (532 citations). György Szarvas has collaborated with scholars based in Hungary, Germany and France. Frequent co-authors include Richárd Farkas, Veronika Vincze, János Csirik, György Móra, Róbert Busa‐Fekete, Iryna Gurevych, Marcus Rohrbach, Michael Stark, Bernt Schiele and Chris Biemann. Their work appears in journals such as BMC Bioinformatics, Machine Learning and Journal of the American Medical Informatics Association.
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.