| Driving semantic parsing from the world’s response J Clarke, D Goldwasser, MW Chang, D Roth Proceedings of the fourteenth conference on computational natural language …, 2010 | 285 | 2010 |
| Modeling learner engagement in MOOCs using probabilistic soft logic A Ramesh, D Goldwasser, B Huang, H Daumé III, L Getoor NIPS workshop on data driven education 21, 62, 2013 | 211 | 2013 |
| Encoding Social Information with Graph Convolutional Networks for Political Perspective Detection in News Media C Li, D Goldwasser Proceedings of the 57th Conference of the Association for Computational …, 2019 | 190 | 2019 |
| Learning latent engagement patterns of students in online courses A Ramesh, D Goldwasser, B Huang, H Daume III, L Getoor Proceedings of the AAAI Conference on Artificial Intelligence 28 (1), 2014 | 168 | 2014 |
| Learning from natural instructions D Goldwasser, D Roth Machine learning 94, 205-232, 2014 | 137 | 2014 |
| Analyzing museum visitors’ behavior patterns M Zancanaro, T Kuflik, Z Boger, D Goren-Bar, D Goldwasser User Modeling 2007: 11th International Conference, UM 2007, Corfu, Greece …, 2007 | 135 | 2007 |
| Understanding MOOC discussion forums using seeded LDA A Ramesh, D Goldwasser, B Huang, H Daumé III, L Getoor Proceedings of the ninth workshop on innovative use of NLP for building …, 2014 | 130 | 2014 |
| Predicting Instructor’s Intervention in MOOC forums S Chaturvedi, D Goldwasser, H Daumé III Proceedings of the 52nd Annual Meeting of the Association for Computational …, 2014 | 106 | 2014 |
| Discriminative learning over constrained latent representations MW Chang, D Goldwasser, D Roth, V Srikumar Human Language Technologies: The 2010 Annual Conference of the North …, 2010 | 98 | 2010 |
| Learning from the ones that got away: Detecting new forms of phishing attacks CN Gutierrez, T Kim, R Della Corte, J Avery, D Goldwasser, M Cinque, ... IEEE Transactions on Dependable and Secure Computing 15 (6), 988-1001, 2018 | 97 | 2018 |
| Tathya: A multi-classifier system for detecting check-worthy statements in political debates A Patwari, D Goldwasser, S Bagchi Proceedings of the 2017 ACM on Conference on Information and Knowledge …, 2017 | 95 | 2017 |
| Confidence driven unsupervised semantic parsing D Goldwasser, R Reichart, J Clarke, D Roth Proceedings of the 49th Annual Meeting of the Association for Computational …, 2011 | 90 | 2011 |
| Classification of moral foundations in microblog political discourse K Johnson, D Goldwasser Proceedings of the 56th annual meeting of the association for computational …, 2018 | 88 | 2018 |
| Structured Output Learning with Indirect Supervision. MW Chang, V Srikumar, D Goldwasser, D Roth ICML, 199-206, 2010 | 77 | 2010 |
| Interactive learning for identifying relevant tweets to support real-time situational awareness LS Snyder, YS Lin, M Karimzadeh, D Goldwasser, DS Ebert IEEE transactions on visualization and computer graphics 26 (1), 558-568, 2019 | 62 | 2019 |
| Uncovering hidden engagement patterns for predicting learner performance in MOOCs A Ramesh, D Goldwasser, B Huang, H Daume III, L Getoor Proceedings of the first ACM conference on Learning@ scale conference, 157-158, 2014 | 61 | 2014 |
| Weakly supervised learning of nuanced frames for analyzing polarization in news media S Roy, D Goldwasser arXiv preprint arXiv:2009.09609, 2020 | 59 | 2020 |
| Relation Alignment for Textual Entailment Recognition. M Sammons, VGV Vydiswaran, T Vieira, N Johri, MW Chang, ... TAC, 2009 | 59 | 2009 |
| Identifying stance by analyzing political discourse on twitter K Johnson, D Goldwasser Proceedings of the First Workshop on NLP and Computational Social Science, 66-75, 2016 | 58 | 2016 |
| Analyzing large collections of open-ended feedback from MOOC learners using LDA topic modeling and qualitative analysis G Nanda, KA Douglas, DR Waller, HE Merzdorf, D Goldwasser IEEE Transactions on Learning Technologies 14 (2), 146-160, 2021 | 56 | 2021 |
Dan RothProfessor of Computer Science, University of Pennsylvania在 seas.upenn.edu 的电子邮件经过验证
