| Knowledge graph embedding via dynamic mapping matrix G Ji, S He, L Xu, K Liu, J Zhao Proceedings of the 53rd annual meeting of the association for computational …, 2015 | 1994 | 2015 |
| Knowledge graph completion with adaptive sparse transfer matrix G Ji, K Liu, S He, J Zhao Proceedings of the AAAI conference on artificial intelligence 30 (1), 2016 | 534 | 2016 |
| How to generate a good word embedding S Lai, K Liu, S He, J Zhao IEEE Intelligent Systems 31 (6), 5-14, 2016 | 493 | 2016 |
| Extracting relational facts by an end-to-end neural model with copy mechanism X Zeng, D Zeng, S He, K Liu, J Zhao Proceedings of the 56th Annual Meeting of the Association for Computational …, 2018 | 481 | 2018 |
| Learning to represent knowledge graphs with gaussian embedding S He, K Liu, G Ji, J Zhao Proceedings of the 24th ACM international on conference on information and …, 2015 | 439 | 2015 |
| Distant supervision for relation extraction with sentence-level attention and entity descriptions G Ji, K Liu, S He, J Zhao Proceedings of the AAAI conference on artificial intelligence 31 (1), 2017 | 433 | 2017 |
| An end-to-end model for question answering over knowledge base with cross-attention combining global knowledge Y Hao, Y Zhang, K Liu, S He, Z Liu, H Wu, J Zhao Proceedings of the 55th Annual Meeting of the Association for Computational …, 2017 | 412 | 2017 |
| A brief overview of ChatGPT: The history, status quo and potential future development T Wu, S He, J Liu, S Sun, K Liu, QL Han, Y Tang IEEE/CAA Journal of Automatica Sinica 10 (5), 1122-1136, 2023 | 266 | 2023 |
| Leveraging framenet to improve automatic event detection S Liu, Y Chen, S He, K Liu, J Zhao Proceedings of the 54th Annual Meeting of the Association for Computational …, 2016 | 148 | 2016 |
| Generating natural answers by incorporating copying and retrieving mechanisms in sequence-to-sequence learning S He, C Liu, K Liu, J Zhao Proceedings of the 55th Annual Meeting of the Association for Computational …, 2017 | 147 | 2017 |
| Learning the extraction order of multiple relational facts in a sentence with reinforcement learning X Zeng, S He, D Zeng, K Liu, S Liu, J Zhao Proceedings of the 2019 conference on empirical methods in natural language …, 2019 | 139 | 2019 |
| Large scaled relation extraction with reinforcement learning X Zeng, S He, K Liu, J Zhao Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 121 | 2018 |
| A joint embedding method for entity alignment of knowledge bases Y Hao, Y Zhang, S He, K Liu, J Zhao Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big …, 2016 | 109 | 2016 |
| A probabilistic soft logic based approach to exploiting latent and global information in event classification S Liu, K Liu, S He, J Zhao Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016 | 87 | 2016 |
| Question answering over knowledge base with neural attention combining global knowledge information Y Zhang, K Liu, S He, G Ji, Z Liu, H Wu, J Zhao arXiv preprint arXiv:1606.00979, 2016 | 78 | 2016 |
| Curriculum Learning for Natural Answer Generation. C Liu, S He, K Liu, J Zhao IJCAI, 4223-4229, 2018 | 71 | 2018 |
| Ontology matching with word embeddings Y Zhang, X Wang, S Lai, S He, K Liu, J Zhao, X Lv Chinese Computational Linguistics and Natural Language Processing Based on …, 2014 | 69 | 2014 |
| Large language models are reasoners with self-verification Y Weng, M Zhu, S He, K Liu, J Zhao arXiv preprint arXiv:2212.09561, 2022 | 60 | 2022 |
| CASIA@ V2: A MLN-based Question Answering System over Linked Data. S He, Y Zhang, K Liu, J Zhao CLEF (Working Notes), 1249-1259, 2014 | 60 | 2014 |
| Learning to represent review with tensor decomposition for spam detection X Wang, K Liu, S He, J Zhao Proceedings of the 2016 conference on empirical methods in natural language …, 2016 | 59 | 2016 |
