Prerequisite Learning with Pre-trained Language and Graph Embedding Models-Reference-Cited by-同舟云学术

Prerequisite Learning with Pre-trained Language and Graph Embedding Models

Published:2021 Issue: Volume: Page:98-108
ISSN:0302-9743
Container-title:Natural Language Processing and Chinese Computing
language:
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Author:

Li Bangqi,Peng Boci,Shao Yifeng,Wang Zhichun

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-88483-3_8

Reference15 articles.

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2. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Minneapolis, Minnesota, pp. 4171–4186. Association for Computational Linguistics (Junuary 2019). https://doi.org/10.18653/v1/N19-1423, https://www.aclweb.org/anthology/N19-1423

3. Fabbri, A., et al.: TutorialBank: a manually-collected corpus for prerequisite chains, survey extraction and resource recommendation. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Melbourne, Australia, pp. 611–620. Association for Computational Linguistics (July 2018). https://doi.org/10.18653/v1/P18-1057, https://www.aclweb.org/anthology/P18-1057

4. Gordon, J., Zhu, L., Galstyan, A., Natarajan, P., Burns, G.: Modeling concept dependencies in a scientific corpus. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Berlin, Germany, pp. 866–875. Association for Computational Linguistics (August 2016). https://doi.org/10.18653/v1/P16-1082, https://www.aclweb.org/anthology/P16-1082

5. Grover, A., Leskovec, J.: Node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 855–864. Association for Computing Machinery, New York (2016). https://doi.org/10.1145/2939672.2939754, https://doi.org/10.1145/2939672.2939754

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