| Graph neural networks: A review of methods and applications J Zhou, G Cui, S Hu, Z Zhang, C Yang, Z Liu, L Wang, C Li, M Sun AI open 1, 57-81, 2020 | 6588 | 2020 |
| Learning entity and relation embeddings for knowledge graph completion Y Lin, Z Liu, M Sun, Y Liu, X Zhu Proceedings of the AAAI conference on artificial intelligence 29 (1), 2015 | 4536 | 2015 |
| ERNIE: Enhanced language representation with informative entities Z Zhang, X Han, Z Liu, X Jiang, M Sun, Q Liu Proceedings of the 57th Annual Meeting of the Association for Computational …, 2019 | 1760 | 2019 |
| Network representation learning with rich text information. C Yang, Z Liu, D Zhao, M Sun, EY Chang Proceedings of the 24th International Conference on Artificial Intelligence …, 2015 | 1367 | 2015 |
| Neural relation extraction with selective attention over instances Y Lin, S Shen, Z Liu, H Luan, M Sun Proceedings of the 54th Annual Meeting of the Association for Computational …, 2016 | 1259 | 2016 |
| Representation learning of knowledge graphs with entity descriptions R Xie, Z Liu, J Jia, H Luan, M Sun Proceedings of the AAAI conference on artificial intelligence 30 (1), 2016 | 839 | 2016 |
| Modeling relation paths for representation learning of knowledge bases Y Lin, Z Liu, H Luan, M Sun, S Rao, S Liu Proceedings of the 2015 Conference on Empirical Methods in Natural Language …, 2015 | 798 | 2015 |
| Fewrel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation X Han, H Zhu, P Yu, Z Wang, Y Yao, Z Liu, M Sun Proceedings of the 2018 Conference on Empirical Methods in Natural Language …, 2018 | 714 | 2018 |
| DocRED: A large-scale document-level relation extraction dataset Y Yao, D Ye, P Li, X Han, Y Lin, Z Liu, Z Liu, L Huang, J Zhou, M Sun Proceedings of the 57th Annual Meeting of the Association for Computational …, 2019 | 607 | 2019 |
| Topical word embeddings Y Liu, Z Liu, TS Chua, M Sun Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015 | 583 | 2015 |
| Parameter-efficient fine-tuning of large-scale pre-trained language models N Ding, Y Qin, G Yang, F Wei, Z Yang, Y Su, S Hu, Y Chen, CM Chan, ... Nature Machine Intelligence 5 (3), 220-235, 2023 | 554 | 2023 |
| Minimum risk training for neural machine translation S Shen, Y Cheng, Z He, W He, H Wu, M Sun, Y Liu Proceedings of the 54th Annual Meeting of the Association for Computational …, 2015 | 515 | 2015 |
| Automatic keyphrase extraction via topic decomposition Z Liu, W Huang, Y Zheng, M Sun Proceedings of the 2010 conference on empirical methods in natural language …, 2010 | 506 | 2010 |
| Ptr: Prompt tuning with rules for text classification X Han, W Zhao, N Ding, Z Liu, M Sun AI Open 3, 182-192, 2022 | 484 | 2022 |
| Clustering to find exemplar terms for keyphrase extraction Z Liu, P Li, Y Zheng, M Sun Proceedings of the 2009 conference on empirical methods in natural language …, 2009 | 439 | 2009 |
| Word-level textual adversarial attacking as combinatorial optimization Y Zang, F Qi, C Yang, Z Liu, M Zhang, Q Liu, M Sun arXiv preprint arXiv:1910.12196, 2019 | 436 | 2019 |
| Iterative Entity Alignment via Joint Knowledge Embeddings. H Zhu, R Xie, Z Liu, M Sun Proceedings of the Twenty-Sixth International Joint Conference on Artificial …, 2017 | 430 | 2017 |
| Toolllm: Facilitating large language models to master 16000+ real-world apis Y Qin, S Liang, Y Ye, K Zhu, L Yan, Y Lu, Y Lin, X Cong, X Tang, B Qian, ... arXiv preprint arXiv:2307.16789, 2023 | 428 | 2023 |
| Openke: An open toolkit for knowledge embedding X Han, S Cao, X Lv, Y Lin, Z Liu, M Sun, J Li Proceedings of the 2018 conference on empirical methods in natural language …, 2018 | 421 | 2018 |
| A unified model for word sense representation and disambiguation X Chen, Z Liu, M Sun Proceedings of the 2014 conference on empirical methods in natural language …, 2014 | 420 | 2014 |
Zhiyuan LiuAssociate Professor, Tsinghua University在 tsinghua.edu.cn 的电子邮件经过验证
