HAEE: Low-Resource Event Detection with Hierarchy-Aware Event Graph Embeddings

Author:

Ding Guoxuan,Guo Xiaobo,Chen Gaode,Wang Lei,Zha Daren

Publisher

Springer Nature Switzerland

Reference36 articles.

1. Bosselut, A., Le Bras, R., Choi, Y.: Dynamic neuro-symbolic knowledge graph construction for zero-shot commonsense question answering. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 4923–4931 (2021)

2. Boyd-Graber, J., Börschinger, B.: What question answering can learn from trivia nerds. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 7422–7435. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.662. https://aclanthology.org/2020.acl-main.662

3. Chen, Y., Xu, L., Liu, K., Zeng, D., Zhao, J.: Event extraction via dynamic multi-pooling convolutional neural networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Beijing, China, pp. 167–176. Association for Computational Linguistics (2015). https://doi.org/10.3115/v1/P15-1017. https://aclanthology.org/P15-1017

4. Deng, S., et al.: Low-resource extraction with knowledge-aware pairwise prototype learning. Knowl.-Based Syst. 235, 107584 (2022)

5. Deng, S., et al.: OntoED: low-resource event detection with ontology embedding. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 2828–2839. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.220. https://aclanthology.org/2021.acl-long.220

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