Looking beyond the surface: A challenge set for reading comprehension over multiple sentences D Khashabi, S Chaturvedi, M Roth, S Upadhyay, D Roth Proceedings of the 2018 Conference of the North American Chapter of the …, 2018 | 475 | 2018 |
Feuding families and former friends: Unsupervised learning for dynamic fictional relationships M Iyyer, A Guha, S Chaturvedi, J Boyd-Graber, H Daumé III Proceedings of the 2016 Conference of the North American Chapter of the …, 2016 | 175 | 2016 |
Bridging the structural gap between encoding and decoding for data-to-text generation C Zhao, M Walker, S Chaturvedi Proceedings of the 58th Annual Meeting of the Association for Computational …, 2020 | 105 | 2020 |
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 | 105 | 2014 |
Story comprehension for predicting what happens next S Chaturvedi, H Peng, D Roth Proceedings of the 2017 Conference on Empirical Methods in Natural Language …, 2017 | 102 | 2017 |
Predicting the impact of scientific concepts using full‐text features K McKeown, H Daume III, S Chaturvedi, J Paparrizos, K Thadani, P Barrio, ... Journal of the Association for Information Science and Technology 67 (11 …, 2016 | 73 | 2016 |
Inferring interpersonal relations in narrative summaries S Srivastava, S Chaturvedi, T Mitchell Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016 | 67 | 2016 |
Modeling evolving relationships between characters in literary novels S Chaturvedi, S Srivastava, H Daume III, C Dyer Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016 | 58* | 2016 |
Unsupervised learning of evolving relationships between literary characters S Chaturvedi, M Iyyer, H Daume III Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 55 | 2017 |
Named entity recognition with partially annotated training data S Mayhew, S Chaturvedi, CT Tsai, D Roth arXiv preprint arXiv:1909.09270, 2019 | 53 | 2019 |
Modeling protagonist emotions for emotion-aware storytelling F Brahman, S Chaturvedi arXiv preprint arXiv:2010.06822, 2020 | 47 | 2020 |
Systems and methods for efficient development of a rule-based system using crowd-sourcing S Chaturvedi, TA Faruquie, LV Subramaniam US Patent 8,635,197, 2014 | 42 | 2014 |
Data cleansing techniques for large enterprise datasets KH Prasad, TA Faruquie, S Joshi, S Chaturvedi, LV Subramaniam, ... 2011 Annual SRII Global Conference, 135-144, 2011 | 42 | 2011 |
Lessons learned from teaching machine learning and natural language processing to high school students N Norouzi, S Chaturvedi, M Rutledge Proceedings of the AAAI conference on artificial intelligence 34 (09), 13397 …, 2020 | 35 | 2020 |
Group‐in‐a‐Box Meta‐Layouts for Topological Clusters and Attribute‐Based Groups: Space‐Efficient Visualizations of Network Communities and Their Ties S Chaturvedi, C Dunne, Z Ashktorab, R Zachariah, B Shneiderman Computer Graphics Forum 33 (8), 52-68, 2014 | 33 | 2014 |
" Let Your Characters Tell Their Story": A Dataset for Character-Centric Narrative Understanding F Brahman, M Huang, O Tafjord, C Zhao, M Sachan, S Chaturvedi arXiv preprint arXiv:2109.05438, 2021 | 31 | 2021 |
Learner Affect through the Looking Glass: Characterization and Detection of Confusion in Online Courses. Z Zeng, S Chaturvedi, S Bhat International Educational Data Mining Society, 2017 | 27 | 2017 |
Ask, and shall you receive? understanding desire fulfillment in natural language text S Chaturvedi, D Goldwasser, H Daume III Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016 | 22 | 2016 |
A joint model for semantic sequences: Frames, entities, sentiments H Peng, S Chaturvedi, D Roth Proceedings of the 21st Conference on Computational Natural Language …, 2017 | 21 | 2017 |
System and method for artificial intelligence story generation allowing content introduction S Chaturvedi, F Brahman, A Petrusca US Patent 11,520,971, 2022 | 20 | 2022 |