| The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes V Vincze, G Szarvas, R Farkas, G Móra, J Csirik BMC bioinformatics 9, 1-9, 2008 | 531 | 2008 |
| The CoNLL-2010 shared task: learning to detect hedges and their scope in natural language text R Farkas, V Vincze, G Móra, J Csirik, G Szarvas Proceedings of the fourteenth conference on computational natural language …, 2010 | 395 | 2010 |
| What helps where–and why? semantic relatedness for knowledge transfer M Rohrbach, M Stark, G Szarvas, I Gurevych, B Schiele 2010 IEEE Computer Society Conference on Computer Vision and Pattern …, 2010 | 356 | 2010 |
| The BioScope corpus: annotation for negation, uncertainty and their scope in biomedical texts G Szarvas, V Vincze, R Farkas, J Csirik Proceedings of the workshop on current trends in biomedical natural language …, 2008 | 243 | 2008 |
| Automatic construction of rule-based ICD-9-CM coding systems R Farkas, G Szarvas BMC bioinformatics 9, 1-9, 2008 | 222 | 2008 |
| On challenges in machine learning model management S Schelter, F Biessmann, T Januschowski, D Salinas, S Seufert, ... | 207 | 2015 |
| State-of-the-art anonymization of medical records using an iterative machine learning framework G Szarvas, R Farkas, R Busa-Fekete Journal of the American Medical Informatics Association 14 (5), 574-580, 2007 | 189 | 2007 |
| A multilingual named entity recognition system using boosting and c4. 5 decision tree learning algorithms G Szarvas, R Farkas, A Kocsor Discovery Science: 9th International Conference, DS 2006, Barcelona, Spain …, 2006 | 179 | 2006 |
| The multilingual amazon reviews corpus P Keung, Y Lu, G Szarvas, NA Smith Proceedings of the 2020 Conference on Empirical Methods in Natural Language …, 2020 | 157 | 2020 |
| Cross-genre and cross-domain detection of semantic uncertainty G Szarvas, V Vincze, R Farkas, G Móra, I Gurevych Computational Linguistics 38 (2), 335-367, 2012 | 135 | 2012 |
| Hedge classification in biomedical texts with a weakly supervised selection of keywords G Szarvas Proceedings of acl-08: HLT, 281-289, 2008 | 124 | 2008 |
| Methods and results of the Hungarian WordNet project M Miháltz, C Hatvani, J Kuti, G Szarvas, J Csirik, G Prószéky, T Váradi Proceedings of GWC 2008, 4th, 2007 | 102 | 2007 |
| A highly accurate Named Entity corpus for Hungarian G Szarvas, R Farkas, L Felföldi, A Kocsor, J Csirik annotation 2, 3.1, 2006 | 65 | 2006 |
| An apple-to-apple comparison of learning-to-rank algorithms in terms of normalized discounted cumulative gain R Busa-Fekete, G Szarvas, T Elteto, B Kégl ECAI 2012-20th European Conference on Artificial Intelligence: Preference …, 2012 | 53 | 2012 |
| Supervised all-words lexical substitution using delexicalized features G Szarvas, C Biemann, I Gurevych Proceedings of the 2013 Conference of the North American Chapter of the …, 2013 | 46 | 2013 |
| Mining multiword terms from Wikipedia S Hartmann, G Szarvas, I Gurevych Semi-Automatic Ontology Development: Processes and Resources, 226-258, 2012 | 41 | 2012 |
| Szövegbányászat D Tikk, R Farkas, ZT Kardkovács, L Kovács, T Répási, G Szarvas, ... Budapest: Typotex, 2007 | 34 | 2007 |
| Combining query translation techniques to improve cross-language information retrieval B Herbert, G Szarvas, I Gurevych European Conference on Information Retrieval, 712-715, 2011 | 31 | 2011 |
| Semi-automated construction of decision rules to predict morbidities from clinical texts R Farkas, G Szarvas, I Hegedűs, A Almási, V Vincze, R Ormándi, ... Journal of the American Medical Informatics Association 16 (4), 601-605, 2009 | 31 | 2009 |
| GYDER: maxent metonymy resolution R Farkas, E Simon, G Szarvas, D Varga Proceedings of the Fourth International Workshop on Semantic Evaluations …, 2007 | 30 | 2007 |
Richárd Farkasresearcher at University of Szeged在 inf.u-szeged.hu 的电子邮件经过验证
