Improve Knowledge Graph Completion for Diagnosing Defects in Main Electrical Equipment

Author:

Huang Jianye,Qian Jian,Chen Yanyu,Lin Rui,Weng Yuyou,Lin Guoqing,Zhang Zhihong

Publisher

Springer Nature Singapore

Reference21 articles.

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2. Gururangan, S., Marasović, A., Swayamdipta, S., et al.: Don’t stop pretraining: adapt language models to domains and tasks. arXiv preprint arXiv:2004.10964 (2020)

3. Houlsby, N., Giurgiu, A., Jastrzebski, S., et al.: Parameter-efficient transfer learning for NLP. In: International Conference on Machine Learning. PMLR, pp. 2790–2799 (2019)

4. Devlin, J., Chang, M.W., Lee, K., et al.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

5. Yang, S., Wang, J., Meng, F., et al.: Text mining techniques for knowledge of defects in power equipment. In: 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT). IEEE, pp. 205–210 (2021)

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