Knowprompt: Knowledge-aware prompt-tuning with synergistic optimization for relation extraction X Chen, N Zhang, X Xie, S Deng, Y Yao, C Tan, F Huang, L Si, H Chen Proceedings of the ACM Web conference 2022, 2778-2788, 2022 | 432 | 2022 |
Document-level relation extraction as semantic segmentation N Zhang, X Chen, X Xie, S Deng, C Tan, M Chen, F Huang, L Si, H Chen arXiv preprint arXiv:2106.03618, 2021 | 207 | 2021 |
Cblue: A chinese biomedical language understanding evaluation benchmark N Zhang, M Chen, Z Bi, X Liang, L Li, X Shang, K Yin, C Tan, J Xu, ... arXiv preprint arXiv:2106.08087, 2021 | 205 | 2021 |
From discrimination to generation: Knowledge graph completion with generative transformer X Xie, N Zhang, Z Li, S Deng, H Chen, F Xiong, M Chen, H Chen Companion Proceedings of the Web Conference 2022, 162-165, 2022 | 96 | 2022 |
Lightner: A lightweight generative framework with prompt-guided attention for low-resource ner X Chen, N Zhang, L Li, X Xie, S Deng, C Tan, F Huang, L Si, H Chen arXiv preprint arXiv:2109.00720, 2021 | 50 | 2021 |
Deepke: A deep learning based knowledge extraction toolkit for knowledge base population N Zhang, X Xu, L Tao, H Yu, H Ye, S Qiao, X Xie, X Chen, Z Li, L Li, ... arXiv preprint arXiv:2201.03335, 2022 | 46 | 2022 |
Reasoning through memorization: Nearest neighbor knowledge graph embeddings P Wang, X Xie, X Wang, N Zhang CCF International Conference on Natural Language Processing and Chinese …, 2023 | 26 | 2023 |
Zekun Xi, Siyuan Cheng, Kangwei Liu, Guozhou Zheng, et al. 2023. Easyedit: An easy-to-use knowledge editing framework for large language models P Wang, N Zhang, X Xie, Y Yao, B Tian, M Wang arXiv preprint arXiv:2308.07269, 2023 | 24 | 2023 |
Easyedit: An easy-to-use knowledge editing framework for large language models P Wang, N Zhang, B Tian, Z Xi, Y Yao, Z Xu, M Wang, S Mao, X Wang, ... arXiv preprint arXiv:2308.07269, 2023 | 20 | 2023 |
Zekun Xi, Siyuan Cheng, Kangwei Liu, Guozhou Zheng, et al. Easyedit: An easy-to-use knowledge editing framework for large language models P Wang, N Zhang, X Xie, Y Yao, B Tian, M Wang arXiv preprint arXiv:2308.07269, 2023 | 18 | 2023 |
Lambdakg: A library for pre-trained language model-based knowledge graph embeddings X Xie, Z Li, X Wang, Z Xi, N Zhang arXiv preprint arXiv:2210.00305, 2022 | 18 | 2022 |
Towards realistic low-resource relation extraction: A benchmark with empirical baseline study X Xu, X Chen, N Zhang, X Xie, X Chen, H Chen arXiv preprint arXiv:2210.10678, 2022 | 12 | 2022 |
Zekun Xi, Siyuan Cheng, Kangwei Liu, Guozhou Zheng, and Huajun Chen. 2023b P Wang, N Zhang, X Xie, Y Yao, B Tian, M Wang Easyedit: An easy-to-use knowledge editing framework for large language …, 0 | 12 | |
Zekun Xi, Siyuan Cheng, Kangwei Liu, Guozhou Zheng, and Huajun Chen. Easyedit: An easy-to-use knowledge editing framework for large language models. CoRR, abs/2308.07269, 2023b … P Wang, N Zhang, X Xie, Y Yao, B Tian, M Wang arXiv preprint arXiv.2308.07269, 0 | 11 | |
Normal vs. adversarial: Salience-based analysis of adversarial samples for relation extraction L Li, X Chen, Z Bi, X Xie, S Deng, N Zhang, C Tan, M Chen, H Chen Proceedings of the 10th International Joint Conference on Knowledge Graphs …, 2021 | 9 | 2021 |
Disentangled contrastive learning for learning robust textual representations X Chen, X Xie, Z Bi, H Ye, S Deng, N Zhang, H Chen Artificial Intelligence: First CAAI International Conference, CICAI 2021 …, 2021 | 6 | 2021 |
Promptkg: A prompt learning framework for knowledge graph representation learning and application X Xie, Z Li, X Wang, S Deng, F Xiong, H Chen, N Zhang arXiv preprint, 2022 | 4 | 2022 |
ZJUKLAB at SemEval-2021 task 4: Negative augmentation with language model for reading comprehension of abstract meaning X Xie, X Chen, X Chen, Y Wang, N Zhang, S Deng, H Chen arXiv preprint arXiv:2102.12828, 2021 | 3 | 2021 |
Knowledge Graph-Based In-Context Learning for Advanced Fault Diagnosis in Sensor Networks X Xie, J Wang, Y Han, W Li Sensors 24 (24), 8086, 2024 | | 2024 |
Towards Robust Textual Representations with Disentangled Contrastive Learning N Zhang, X Chen, X Xie, S Deng, Y Jia, Z Yuan, H Chen | | |