| Hateful symbols or hateful people? predictive features for hate speech detection on twitter Z Waseem, D Hovy Proceedings of the NAACL student research workshop, 88-93, 2016 | 1953 | 2016 |
| The Social Impact of Natural Language Processing D Hovy, SL Spruit ACL, 2016 | 418 | 2016 |
| Learning whom to trust with MACE D Hovy, T Berg-Kirkpatrick, A Vaswani, E Hovy Proceedings of the 2013 Conference of the North American Chapter of the …, 2013 | 392 | 2013 |
| Multitask Learning for Mental Health Conditions with Limited Social Media Data A Benton, M Mitchell, D Hovy Proceedings of the 15th Conference of the European Chapter of the …, 2017 | 298* | 2017 |
| Predictive biases in natural language processing models: A conceptual framework and overview D Shah, HA Schwartz, D Hovy Proceedings of ACL, 2020 | 277 | 2020 |
| Pre-training is a hot topic: Contextualized document embeddings improve topic coherence F Bianchi, S Terragni, D Hovy ACL-IJCNLP, 759–766, 2021 | 269 | 2021 |
| Personality traits on twitter—or—how to get 1,500 personality tests in a week B Plank, D Hovy Proceedings of the 6th workshop on computational approaches to subjectivity …, 2015 | 234 | 2015 |
| Five sources of bias in natural language processing D Hovy, S Prabhumoye Language and linguistics compass 15 (8), e12432, 2021 | 219 | 2021 |
| Demographic Factors Improve Classification Performance D Hovy Proceedings of ACL, 2015 | 217 | 2015 |
| Tagging Performance Correlates Negatively with Author Age D Hovy, A Søgaard ACL, 2015 | 159* | 2015 |
| The importance of modeling social factors of language: Theory and practice D Hovy, D Yang Proceedings of the 2021 Conference of the North American Chapter of the …, 2021 | 152 | 2021 |
| Cross-lingual contextualized topic models with zero-shot learning F Bianchi, S Terragni, D Hovy, D Nozza, E Fersini arXiv preprint arXiv:2004.07737, 2020 | 149 | 2020 |
| Cross-lingual syntactic variation over age and gender A Johannsen, D Hovy, A Søgaard Proceedings of the nineteenth conference on computational natural language …, 2015 | 125 | 2015 |
| Learning from disagreement: A survey AN Uma, T Fornaciari, D Hovy, S Paun, B Plank, M Poesio Journal of Artificial Intelligence Research 72, 1385-1470, 2021 | 123 | 2021 |
| Learning part-of-speech taggers with inter-annotator agreement loss B Plank, D Hovy, A Søgaard Proceedings of the 14th Conference of the European Chapter of the …, 2014 | 122 | 2014 |
| User review sites as a resource for large-scale sociolinguistic studies D Hovy, A Johannsen, A Søgaard Proceedings of the 24th international conference on World Wide Web, 452-461, 2015 | 113 | 2015 |
| Comparing Bayesian models of annotation S Paun, B Carpenter, J Chamberlain, D Hovy, U Kruschwitz, M Poesio Transactions of the Association for Computational Linguistics 6, 571-585, 2018 | 110 | 2018 |
| Solving Electrical Networks to incorporate Supervision in Random Walks M Sachan, D Hovy, E Hovy Proceedings of the 22nd International Conference on World Wide Web, 109-110, 2013 | 110 | 2013 |
| Identifying metaphorical word use with tree kernels D Hovy, S Srivastava, SK Jauhar, M Sachan, K Goyal, H Li, W Sanders, ... Proceedings of the First Workshop on Metaphor in NLP, 52-57, 2013 | 109 | 2013 |
| What the [mask]? making sense of language-specific BERT models D Nozza, F Bianchi, D Hovy arXiv preprint arXiv:2003.02912, 2020 | 108 | 2020 |
Federico BianchiStanford University在 stanford.edu 的电子邮件经过验证
