| Reading tea leaves: How humans interpret topic models J Chang, J Boyd-Graber, S Gerrish, C Wang, DM Blei Neural Information Processing Systems (NIPS) 31, 2009 | 3797 | 2009 |
| Deep unordered composition rivals syntactic methods for text classification M Iyyer, V Manjunatha, J Boyd-Graber, H Daumé III Proceedings of the 53rd annual meeting of the association for computational …, 2015 | 1189 | 2015 |
| Interactive topic modeling Y Hu, J Boyd-Graber, B Satinoff, A Smith Machine learning 95 (3), 423-469, 2014 | 537 | 2014 |
| A neural network for factoid question answering over paragraphs M Iyyer, J Boyd-Graber, L Claudino, R Socher, H Daumé III Proceedings of the 2014 conference on empirical methods in natural language …, 2014 | 488 | 2014 |
| Applications of topic models J Boyd-Graber, Y Hu, D Mimno Foundations and Trends® in Information Retrieval 11 (2-3), 143-296, 2017 | 485 | 2017 |
| Opponent modeling in deep reinforcement learning H He, J Boyd-Graber, K Kwok, H Daumé III International conference on machine learning, 1804-1813, 2016 | 457 | 2016 |
| Political ideology detection using recursive neural networks M Iyyer, P Enns, J Boyd-Graber, P Resnik Proceedings of the 52nd annual meeting of the Association for Computational …, 2014 | 451 | 2014 |
| Pathologies of neural models make interpretations difficult S Feng, E Wallace, A Grissom II, M Iyyer, P Rodriguez, J Boyd-Graber arXiv preprint arXiv:1804.07781, 2018 | 413 | 2018 |
| Prompting gpt-3 to be reliable C Si, Z Gan, Z Yang, S Wang, J Wang, J Boyd-Graber, L Wang arXiv preprint arXiv:2210.09150, 2022 | 378 | 2022 |
| A topic model for word sense disambiguation J Boyd-Graber, D Blei, X Zhu Conference on Empirical Methods in Natural Language Processing (EMNLP), 1024 …, 2007 | 354 | 2007 |
| Beyond LDA: exploring supervised topic modeling for depression-related language in Twitter P Resnik, W Armstrong, L Claudino, T Nguyen, VA Nguyen, ... Proceedings of the 2nd workshop on computational linguistics and clinical …, 2015 | 337 | 2015 |
| Care and feeding of topic models: Problems, diagnostics, and improvements J Boyd-Graber, D Mimno, D Newman Handbook of mixed membership models and their applications 225255, 2014 | 302 | 2014 |
| Syntactic topic models J Boyd-Graber, DM Blei Neural Information Processing Systems (NIPS), 2008 | 297 | 2008 |
| Can you unpack that? learning to rewrite questions-in-context A Elgohary, D Peskov, J Boyd-Graber Can You Unpack That? Learning to Rewrite Questions-in-Context, 2019 | 285 | 2019 |
| Cold-start active learning through self-supervised language modeling M Yuan, HT Lin, J Boyd-Graber arXiv preprint arXiv:2010.09535, 2020 | 261 | 2020 |
| Climate-fever: A dataset for verification of real-world climate claims T Diggelmann, J Boyd-Graber, J Bulian, M Ciaramita, M Leippold arXiv preprint arXiv:2012.00614, 2020 | 259 | 2020 |
| Multilingual topic models for unaligned text J Boyd-Graber, D Blei arXiv preprint arXiv:1205.2657, 2012 | 238 | 2012 |
| Is automated topic model evaluation broken? the incoherence of coherence A Hoyle, P Goel, A Hian-Cheong, D Peskov, J Boyd-Graber, P Resnik Advances in neural information processing systems 34, 2018-2033, 2021 | 223 | 2021 |
| Mr. LDA: A flexible large scale topic modeling package using variational inference in mapreduce K Zhai, J Boyd-Graber, N Asadi, ML Alkhouja Proceedings of the 21st international conference on World Wide Web, 879-888, 2012 | 209 | 2012 |
| Trick me if you can: Human-in-the-loop generation of adversarial examples for question answering E Wallace, P Rodriguez, S Feng, I Yamada, J Boyd-Graber Transactions of the Association for Computational Linguistics 7, 387-401, 2019 | 207 | 2019 |
Philip ResnikProfessor, Linguistics and UMIACS, University of MarylandVerified email at umd.edu
