| Identifying sarcasm in twitter: a closer look R González-Ibánez, S Muresan, N Wacholder Proceedings of the 49th annual meeting of the association for computational …, 2011 | 886 | 2011 |
| Where is your evidence: Improving fact-checking by justification modeling T Alhindi, S Petridis, S Muresan Proceedings of the first workshop on fact extraction and verification (FEVER …, 2018 | 206 | 2018 |
| Generalizing word lattice translation C Dyer, S Muresan, P Resnik Proceedings of ACL-08: HLT, 1012-1020, 2008 | 204 | 2008 |
| Sarcastic or not: Word embeddings to predict the literal or sarcastic meaning of words D Ghosh, W Guo, S Muresan proceedings of the 2015 conference on empirical methods in natural language …, 2015 | 146 | 2015 |
| Analyzing the semantic types of claims and premises in an online persuasive forum C Hidey, E Musi, A Hwang, S Muresan, K McKeown Proceedings of the 4th Workshop on Argument Mining, 2017 | 140 | 2017 |
| AMPERSAND: Argument mining for PERSuAsive oNline discussions T Chakrabarty, C Hidey, S Muresan, K McKeown, A Hwang arXiv preprint arXiv:2004.14677, 2020 | 129 | 2020 |
| Analyzing argumentative discourse units in online interactions D Ghosh, S Muresan, N Wacholder, M Aakhus, M Mitsui Proceedings of the first workshop on argumentation mining, 39-48, 2014 | 127 | 2014 |
| Fine-tuned language models are continual learners T Scialom, T Chakrabarty, S Muresan arXiv preprint arXiv:2205.12393, 2022 | 124* | 2022 |
| COVID-fact: Fact extraction and verification of real-world claims on COVID-19 pandemic A Saakyan, T Chakrabarty, S Muresan arXiv preprint arXiv:2106.03794, 2021 | 121 | 2021 |
| Art or artifice? large language models and the false promise of creativity T Chakrabarty, P Laban, D Agarwal, S Muresan, CS Wu Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems …, 2024 | 112 | 2024 |
| Sarcasm analysis using conversation context D Ghosh, AR Fabbri, S Muresan Computational Linguistics 44 (4), 755-792, 2018 | 104 | 2018 |
| The role of conversation context for sarcasm detection in online interactions D Ghosh, AR Fabbri, S Muresan arXiv preprint arXiv:1707.06226, 2017 | 95 | 2017 |
| A Method for Automatically Building and Evaluating Dictionary Resources. S Muresan, J Klavans LREC 2, 231-234, 2002 | 93 | 2002 |
| Evaluation of the DEFINDER system for fully automatic glossary construction JL Klavans, S Muresan Proceedings of the AMIA Symposium, 324, 2001 | 91 | 2001 |
| Combining linguistic and machine learning techniques for email summarization S Muresan, E Tzoukermann, JL Klavans Proceedings of the ACL 2001 Workshop on Computational Natural Language …, 2001 | 90 | 2001 |
| Coarse-grained argumentation features for scoring persuasive essays D Ghosh, A Khanam, Y Han, S Muresan Proceedings of the 54th Annual Meeting of the Association for Computational …, 2016 | 87 | 2016 |
| : Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge T Chakrabarty, D Ghosh, S Muresan, N Peng arXiv preprint arXiv:2004.13248, 2020 | 85 | 2020 |
| DEFINDER: Rule-based methods for the extraction of medical terminology and their associated definitions from on-line text JL Klavans, S Muresan Proceedings of the AMIA Symposium, 1049, 2000 | 85 | 2000 |
| MERMAID: Metaphor generation with symbolism and discriminative decoding T Chakrabarty, X Zhang, S Muresan, N Peng arXiv preprint arXiv:2103.06779, 2021 | 74 | 2021 |
| Identification of nonliteral language in social media: A case study on sarcasm S Muresan, R Gonzalez‐Ibanez, D Ghosh, N Wacholder Journal of the Association for Information Science and Technology 67 (11 …, 2016 | 74 | 2016 |
Tuhin ChakrabartyStony Brook University , Department of Computer ScienceVerified email at columbia.edu
