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György Szarvas
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2020 – today
- 2024
- [c31]Magdalena Kaiser, Patrick Ernst, György Szarvas:
Learning from Relevant Subgoals in Successful Dialogs using Iterative Training for Task-oriented Dialog Systems. EMNLP (Findings) 2024: 6236-6246 - 2023
- [c30]Aditya Srikanth Veerubhotla, Lahari Poddar, Jun Yin, György Szarvas, Sharanya Eswaran:
Few Shot Rationale Generation using Self-Training with Dual Teachers. ACL (Findings) 2023: 4825-4838 - [i5]Aditya Srikanth Veerubhotla, Lahari Poddar, Jun Yin, György Szarvas, Sharanya Eswaran:
Few Shot Rationale Generation using Self-Training with Dual Teachers. CoRR abs/2306.03315 (2023) - 2022
- [c29]Jiahuan Pei, Cheng Wang, György Szarvas:
Transformer Uncertainty Estimation with Hierarchical Stochastic Attention. AAAI 2022: 11147-11155 - [c28]Cheng Wang, Jorge Balazs, György Szarvas, Patrick Ernst, Lahari Poddar, Pavel Danchenko:
Calibrating Imbalanced Classifiers with Focal Loss: An Empirical Study. EMNLP (Industry Track) 2022: 145-153 - [c27]Lahari Poddar, György Szarvas, Cheng Wang, Jorge Balazs, Pavel Danchenko, Patrick Ernst:
Deploying a Retrieval based Response Model for Task Oriented Dialogues. EMNLP (Industry Track) 2022: 169-178 - [i4]Lahari Poddar, György Szarvas, Cheng Wang, Jorge Balazs, Pavel Danchenko, Patrick Ernst:
Deploying a Retrieval based Response Model for Task Oriented Dialogues. CoRR abs/2210.14379 (2022) - 2021
- [i3]Jiahuan Pei, Cheng Wang, György Szarvas:
Transformer Uncertainty Estimation with Hierarchical Stochastic Attention. CoRR abs/2112.13776 (2021) - 2020
- [c26]Phillip Keung, Yichao Lu, György Szarvas, Noah A. Smith:
The Multilingual Amazon Reviews Corpus. EMNLP (1) 2020: 4563-4568 - [i2]Phillip Keung, Yichao Lu, György Szarvas, Noah A. Smith:
The Multilingual Amazon Reviews Corpus. CoRR abs/2010.02573 (2020)
2010 – 2019
- 2019
- [i1]Lahari Poddar, György Szarvas, Lea Frermann:
A Probabilistic Framework for Learning Domain Specific Hierarchical Word Embeddings. CoRR abs/1910.07333 (2019) - 2017
- [c25]Lea Frermann, György Szarvas:
Inducing Semantic Micro-Clusters from Deep Multi-View Representations of Novels. EMNLP 2017: 1873-1883 - 2013
- [j8]Róbert Busa-Fekete, Balázs Kégl, Tamás Éltetö, György Szarvas:
Tune and mix: learning to rank using ensembles of calibrated multi-class classifiers. Mach. Learn. 93(2-3): 261-292 (2013) - [c24]György Szarvas, Róbert Busa-Fekete, Eyke Hüllermeier:
Learning to Rank Lexical Substitutions. EMNLP 2013: 1926-1932 - [c23]György Szarvas, Iryna Gurevych:
Uncertainty Detection for Natural Language Watermarking. IJCNLP 2013: 1188-1194 - [c22]György Szarvas, Chris Biemann, Iryna Gurevych:
Supervised All-Words Lexical Substitution using Delexicalized Features. HLT-NAACL 2013: 1131-1141 - 2012
- [j7]György Szarvas, Veronika Vincze, Richárd Farkas
, György Móra, Iryna Gurevych:
Cross-Genre and Cross-Domain Detection of Semantic Uncertainty. Comput. Linguistics 38(2): 335-367 (2012) - 2011
- [j6]Veronika Vincze, György Szarvas, György Móra, Tomoko Ohta, Richárd Farkas:
Linguistic scope-based and biological event-based speculation and negation annotations in the BioScope and Genia Event corpora. J. Biomed. Semant. 2(S-5): S8 (2011) - [c21]György Szarvas, Torsten Zesch, Iryna Gurevych:
Combining Heterogeneous Knowledge Resources for Improved Distributional Semantic Models. CICLing (1) 2011: 289-303 - [c20]Benjamin Herbert, György Szarvas, Iryna Gurevych:
Combining Query Translation Techniques to Improve Cross-Language Information Retrieval. ECIR 2011: 712-715 - [c19]Róbert Busa-Fekete, Balázs Kégl, Tamás Éltetö, György Szarvas:
A Robust Ranking Methodology Based on Diverse Calibration of AdaBoost. ECML/PKDD (1) 2011: 263-279 - [c18]Róbert Busa-Fekete, Balázs Kégl, Tamás Éltetö, György Szarvas:
Ranking by calibrated AdaBoost. Yahoo! Learning to Rank Challenge 2011: 37-48 - 2010
- [c17]Richárd Farkas, Veronika Vincze, György Móra, János Csirik, György Szarvas:
The CoNLL-2010 Shared Task: Learning to Detect Hedges and their Scope in Natural Language Text. CoNLL Shared Task 2010: 1-12 - [c16]Marcus Rohrbach, Michael Stark, György Szarvas, Iryna Gurevych, Bernt Schiele
What helps where - and why? Semantic relatedness for knowledge transfer. CVPR 2010: 910-917 - [c15]Marcus Rohrbach, Michael Stark, György Szarvas, Bernt Schiele:
Combining Language Sources and Robust Semantic Relatedness for Attribute-Based Knowledge Transfer. ECCV Workshops (1) 2010: 15-28 - [c14]György Szarvas, Iryna Gurevych:
TUD: Semantic Relatedness for Relation Classification. SemEval@ACL 2010: 210-213 - [c13]Veronika Vincze, György Szarvas, György Móra, Tomoko Ohta, Richárd Farkas:
Linguistic scope-based and biological event-based speculation and negation annotations in the Genia Event and BioScope corpora. Semantic Mining in Biomedicine 2010 - [e1]Richárd Farkas, Veronika Vincze, György Szarvas, György Móra, János Csirik:
Proceedings of the Fourteenth Conference on Computational Natural Language Learning: Shared Task, CoNLL 2010, Uppsala, Sweden, July 15-16, 2010. ACL 2010, ISBN 978-1-932432-84-8 [contents]
2000 – 2009
- 2009
- [j5]Richárd Farkas
Research Paper: Semi-automated Construction of Decision Rules to Predict Morbidities from Clinical Texts. J. Am. Medical Informatics Assoc. 16(4): 601-605 (2009) - [c12]György Móra, Richárd Farkas, György Szarvas, Zsolt Molnár:
Exploring ways beyond the simple supervised learning approach for biological event extraction. BioNLP@HLT-NAACL (Shared Task) 2009: 137-140 - [c11]Benjamin Herbert, György Szarvas, Iryna Gurevych:
Prior Art Search Using International Patent Classification Codes and All-Claims-Queries. CLEF (1) 2009: 452-459 - [c10]György Szarvas, Benjamin Herbert, Iryna Gurevych:
Prior Art Search using International Patent Classification Codes and All-Claims-Queries. CLEF (Working Notes) 2009 - 2008
- [j4]Veronika Vincze, György Szarvas, Richárd Farkas
The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes. BMC Bioinform. 9(S-11) (2008) - [j3]Richárd Farkas
Automatic construction of rule-based ICD-9-CM coding systems. BMC Bioinform. 9(S-3) (2008) - [c9]György Szarvas:
Hedge Classification in Biomedical Texts with a Weakly Supervised Selection of Keywords. ACL 2008: 281-289 - [c8]György Szarvas, Veronika Vincze, Richárd Farkas, János Csirik:
The BioScope corpus: annotation for negation, uncertainty and their scope in biomedical texts. BioNLP 2008: 38-45 - [c7]Veronika Vincze, György Szarvas, Attila Almási, Dóra Szauter, Róbert Ormándi, Richárd Farkas, Csaba Hatvani, János Csirik:
Hungarian Word-Sense Disambiguated Corpus. LREC 2008 - [c6]Richárd Farkas
Web-Based Lemmatisation of Named Entities. TSD 2008: 53-60 - 2007
- [j2]György Szarvas, Richárd Farkas, Róbert Busa-Fekete:
Research Paper: State-of-the-art Anonymization of Medical Records Using an Iterative Machine Learning Framework. J. Am. Medical Informatics Assoc. 14(5): 574-580 (2007) - [c5]Richárd Farkas
Improving a State-of-the-Art Named Entity Recognition System Using the World Wide Web. ICDM 2007: 163-172 - [c4]Richárd Farkas, Eszter Simon, György Szarvas, Dániel Varga:
GYDER: Maxent Metonymy Resolution. SemEval@ACL 2007: 161-164 - 2006
- [j1]Richárd Farkas, György Szarvas, András Kocsor:
Named Entity Recognition for Hungarian Using Various Machine Learning Algorithms. Acta Cybern. 17(3): 633-646 (2006) - [c3]György Szarvas, Richárd Farkas
A Multilingual Named Entity Recognition System Using Boosting and C4.5 Decision Tree Learning Algorithms. Discovery Science 2006: 267-278 - [c2]György Szarvas, Richárd Farkas, László Felföldi, András Kocsor, János Csirik:
A highly accurate Named Entity corpus for Hungarian. LREC 2006: 1957-1960 - 2004
- [c1]István Juhos, György Szarvas:
Intelligent Forecast with Dimension Reduction. WSC 2004: 279-292
Coauthor Index
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last updated on 2024-11-19 20:49 CET by the dblp team
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