| SenticNet 3: a common and common-sense knowledge base for cognition-driven sentiment analysis E Cambria, D Olsher, D Rajagopal Proceedings of the AAAI conference on artificial intelligence 28 (1), 2014 | 593 | 2014 |
| Gated-attention architectures for task-oriented language grounding DS Chaplot, KM Sathyendra, RK Pasumarthi, D Rajagopal, ... Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 176 | 2018 |
| Big social data analysis E Cambria, D Rajagopal, D Olsher, D Das Big data computing 13, 401-414, 2013 | 123 | 2013 |
| A graph-based approach to commonsense concept extraction and semantic similarity detection D Rajagopal, E Cambria, D Olsher, K Kwok Proceedings of the 22nd International Conference on World Wide Web, 565-570, 2013 | 122 | 2013 |
| Generating questions and multiple-choice answers using semantic analysis of texts J Araki, D Rajagopal, S Sankaranarayanan, S Holm, Y Yamakawa, ... Proceedings of COLING 2016, the 26th International Conference on …, 2016 | 92 | 2016 |
| Simple and effective semi-supervised question answering B Dhingra, D Pruthi, D Rajagopal Proceedings of the 2018 Conference of the North {A}merican Chapter of the …, 2018 | 83 | 2018 |
| Selfexplain: A self-explaining architecture for neural text classifiers D Rajagopal, V Balachandran, E Hovy, Y Tsvetkov Proceedings of the 2021 Conference on Empirical Methods in Natural Language …, 2021 | 72 | 2021 |
| A dataset for tracking entities in open domain procedural text N Tandon, K Sakaguchi, BD Mishra, D Rajagopal, P Clark, M Guerquin, ... Proceedings of the 2020 Conference on Empirical Methods in Natural Language …, 2020 | 49 | 2020 |
| Structsum: Incorporating latent and explicit sentence dependencies for single document summarization V Balachandran, A Pagnoni, JY Lee, D Rajagopal, J Carbonell, ... Proceedings of the 16th Conference of the European Chapter of the …, 2021 | 31* | 2021 |
| Commonsense-based topic modeling D Rajagopal, D Olsher, E Cambria, K Kwok Proceedings of the second international workshop on issues of sentiment …, 2013 | 31 | 2013 |
| Think about it! Improving defeasible reasoning by first modeling the question scenario A Madaan, N Tandon, D Rajagopal, P Clark, Y Yang, E Hovy Proceedings of the 2021 Conference on Empirical Methods in Natural Language …, 2021 | 27 | 2021 |
| GECKA: game engine for commonsense knowledge acquisition E Cambria, D Rajagopal, K Kwok, J Sepulveda The Twenty-Eighth International Flairs Conference, 2015 | 27 | 2015 |
| What-if I ask you to explain: Explaining the effects of perturbations in procedural text D Rajagopal, N Tandon, B Dalvi, P Clark, E Hovy Findings of the Association for Computational Linguistics: EMNLP 2020, 3345 …, 2020 | 20 | 2020 |
| Eigen: Event influence generation using pre-trained language models A Madaan, D Rajagopal, Y Yang, A Ravichander, E Hovy, S Prabhumoye arXiv preprint arXiv:2010.11764, 2020 | 17 | 2020 |
| Automix: Automatically mixing language models P Aggarwal, A Madaan, A Anand, SP Potharaju, S Mishra, P Zhou, ... arXiv preprint arXiv:2310.12963, 2023 | 16 | 2023 |
| How Far Can We Extract Diverse Perspectives from Large Language Models? Criteria-Based Diversity Prompting! SA Hayati, M Lee, D Rajagopal, D Kang arXiv preprint arXiv:2311.09799, 2023 | 13 | 2023 |
| Counterfactual data augmentation improves factuality of abstractive summarization D Rajagopal, S Shakeri, CN Santos, E Hovy, CC Chang arXiv preprint arXiv:2205.12416, 2022 | 12 | 2022 |
| Could you give me a hint? Generating inference graphs for defeasible reasoning A Madaan, D Rajagopal, N Tandon, Y Yang, E Hovy Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 …, 2021 | 12 | 2021 |
| Conditional set generation using seq2seq models A Madaan, D Rajagopal, N Tandon, Y Yang, A Bosselut arXiv preprint arXiv:2205.12485, 2022 | 9 | 2022 |
| Modeling the relationship between user comments and edits in document revision X Zhang, D Rajagopal, M Gamon, SK Jauhar, CT Lu Proceedings of the 2019 Conference on Empirical Methods in Natural Language …, 2019 | 8 | 2019 |
Erik CambriaProfessor of Artificial Intelligence, CCDS, NTU SingaporeBestätigte E-Mail-Adresse bei ntu.edu.sg