| Self-training from labeled features for sentiment analysis Y He, D Zhou Information Processing & Management 47 (4), 606-616, 2011 | 256 | 2011 |
| Extracting interactions between proteins from the literature D Zhou, Y He Journal of biomedical informatics 41 (2), 393-407, 2008 | 132 | 2008 |
| Emotion distribution learning from texts D Zhou, X Zhang, Y Zhou, Q Zhao, X Geng Proceedings of the 2016 Conference on Empirical Methods in Natural Language …, 2016 | 125 | 2016 |
| Biomedical relation extraction: from binary to complex D Zhou, D Zhong, Y He Computational and mathematical methods in medicine 2014, 2014 | 98 | 2014 |
| An unsupervised framework of exploring events on twitter: Filtering, extraction and categorization D Zhou, L Chen, Y He Proceedings of the AAAI conference on artificial intelligence 29 (1), 2015 | 94 | 2015 |
| Position-aware deep multi-task learning for drug–drug interaction extraction D Zhou, L Miao, Y He Artificial intelligence in medicine 87, 1-8, 2018 | 76 | 2018 |
| Topic-driven and knowledge-aware transformer for dialogue emotion detection L Zhu, G Pergola, L Gui, D Zhou, Y He arXiv preprint arXiv:2106.01071, 2021 | 74 | 2021 |
| Event trigger identification for biomedical events extraction using domain knowledge D Zhou, D Zhong, Y He Bioinformatics 30 (11), 1587-1594, 2014 | 72 | 2014 |
| Atm: Adversarial-neural topic model R Wang, D Zhou, Y He Information Processing & Management 56 (6), 102098, 2019 | 69 | 2019 |
| Neural Topic Modeling with Bidirectional Adversarial Training R Wang, X Hu, D Zhou, Y He, Y Xiong, C Ye, H Xu ACL 2020, 2020 | 66 | 2020 |
| A survey on opinion mining: From stance to product aspect R Wang, D Zhou, M Jiang, J Si, Y Yang IEEE Access 7, 41101-41124, 2019 | 64 | 2019 |
| Potential value of plasma amyloid-β, total tau, and neurofilament light for identification of early Alzheimer’s disease Y Shi, X Lu, L Zhang, H Shu, L Gu, Z Wang, L Gao, J Zhu, H Zhang, ... ACS Chemical Neuroscience 10 (8), 3479-3485, 2019 | 47 | 2019 |
| A semi-supervised learning framework for biomedical event extraction based on hidden topics D Zhou, D Zhong Artificial intelligence in medicine 64 (1), 51-58, 2015 | 39 | 2015 |
| A simple bayesian modelling approach to event extraction from twitter D Zhou, L Chen, Y He the annual meeting of the Association for Computational Linguistics (ACL), 0, 2014 | 39 | 2014 |
| Open Event Extraction from Online Text using a Generative Adversarial Network R Wang, D Zhou, Y He EMNLP-IJCNLP 2019, 2019 | 37 | 2019 |
| Neural opinion dynamics model for the prediction of user-level stance dynamics L Zhu, Y He, D Zhou Information Processing & Management 57 (2), 102031, 2020 | 36 | 2020 |
| Discriminative training of the hidden vector state model for semantic parsing D Zhou, Y He IEEE Transactions on Knowledge and Data Engineering 21 (1), 66-77, 2008 | 35 | 2008 |
| Hidden topic–emotion transition model for multi-level social emotion detection D Tang, Z Zhang, Y He, C Lin, D Zhou Knowledge-Based Systems 164, 426-435, 2019 | 32 | 2019 |
| Sparsity Conditional Energy Label Distribution Learning for Age Estimation X Yang, X Geng, D Zhou the 25th International Joint Conference on Artificial Intelligence IJCAI-16, 2016 | 32 | 2016 |
| Relevant emotion ranking from text constrained with emotion relationships D Zhou, Y Yang, Y He NAACL, 561-571, 2018 | 29 | 2018 |
Yulan HeProfessor, King's College London; Turing AI FellowVerified email at kcl.ac.uk
