Simlex-999: Evaluating semantic models with (genuine) similarity estimation F Hill, R Reichart, A Korhonen Computational Linguistics 41 (4), 665-695, 2015 | 1578 | 2015 |
Modeling the detection of textual cyberbullying K Dinakar, R Reichart, H Lieberman Proceedings of the International AAAI Conference on Web and Social Media 5 …, 2011 | 758 | 2011 |
The hitchhiker’s guide to testing statistical significance in natural language processing R Dror, G Baumer, S Shlomov, R Reichart Proceedings of the 56th annual meeting of the association for computational …, 2018 | 419 | 2018 |
Thinking ahead: spontaneous prediction in context as a keystone of language in humans and machines A Goldstein, Z Zada, E Buchnik, M Schain, A Price, B Aubrey, SA Nastase, ... BioRxiv, 2020.12. 02.403477, 2020 | 313* | 2020 |
SimVerb-3500: A large-scale evaluation set of verb similarity D Gerz, I Vulić, F Hill, R Reichart, A Korhonen Proceedings of EMNLP 2016, 2016 | 305 | 2016 |
Semantic specialisation of distributional word vector spaces using monolingual and cross-lingual constraints N Mrkšić, I Vulić, DÓ Séaghdha, I Leviant, R Reichart, M Gašić, ... Transactions of the Association for Computational Linguistics 5, 309--324, 2017 | 251 | 2017 |
Causal inference in natural language processing: Estimation, prediction, interpretation and beyond A Feder, KA Keith, E Manzoor, R Pryzant, D Sridhar, Z Wood-Doughty, ... Transactions of the Association for Computational Linguistics 10, 1138-1158, 2022 | 208 | 2022 |
Deep dominance-how to properly compare deep neural models R Dror, S Shlomov, R Reichart Proceedings of the 57th Annual Meeting of the Association for Computational …, 2019 | 160 | 2019 |
The Turker blues: Hidden factors behind increased depression rates among Amazon’s Mechanical Turkers Y Ophir, I Sisso, CSC Asterhan, R Tikochinski, R Reichart Clinical Psychological Science 8 (1), 65-83, 2020 | 146 | 2020 |
Modeling language variation and universals: A survey on typological linguistics for natural language processing EM Ponti, H O’horan, Y Berzak, I Vulić, R Reichart, T Poibeau, E Shutova, ... Computational Linguistics 45 (3), 559-601, 2019 | 146 | 2019 |
Causalm: Causal model explanation through counterfactual language models A Feder, N Oved, U Shalit, R Reichart Computational Linguistics 47 (2), 333-386, 2021 | 143 | 2021 |
Symmetric pattern based word embeddings for improved word similarity prediction R Schwartz, R Reichart, A Rappoport Proceedings of the nineteenth conference on computational natural language …, 2015 | 141 | 2015 |
Pada: A prompt-based autoregressive approach for adaptation to unseen domains E Ben-David, N Oved, R Reichart arXiv preprint arXiv:2102.12206 3, 2021 | 136* | 2021 |
Pivot based language modeling for improved neural domain adaptation Y Ziser, R Reichart Proceedings of the 2018 Conference of the North American Chapter of the …, 2018 | 134 | 2018 |
Self-training for enhancement and domain adaptation of statistical parsers trained on small datasets R Reichart, A Rappoport Proceedings of the 45th Annual Meeting of the Association of Computational …, 2007 | 126 | 2007 |
Multi-task active learning for linguistic annotations R Reichart, K Tomanek, U Hahn, A Rappoport Proceedings of ACL-08: HLT, 861-869, 2008 | 121 | 2008 |
Neural structural correspondence learning for domain adaptation Y Ziser, R Reichart Proceedings of the 21st Conference on Computational Natural Language …, 2016 | 114 | 2016 |
Separated by an Un-common Language: Towards Judgment Language Informed Vector Space Modeling I Leviant, R Reichart arXiv preprint arxiv:1508.00106, 2015 | 113* | 2015 |
Deep neural networks detect suicide risk from textual facebook posts Y Ophir, R Tikochinski, CSC Asterhan, I Sisso, R Reichart Scientific reports 10 (1), 16685, 2020 | 103 | 2020 |
An unsupervised model for instance level subcategorization acquisition S Baker, R Reichart, A Korhonen Proceedings of the 2014 conference on empirical methods in natural language …, 2014 | 102 | 2014 |