EADRE: Event-type Aware Dynamic Representation of Entities in Document-level Event Extraction
Author:
Affiliation:
1. Shanxi University, School of Computer and Information Technology, Taiyuan, China
2. Shanxi University, Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Taiyuan, China
Abstract
Publisher
Association for Computing Machinery (ACM)
Link
https://dl.acm.org/doi/pdf/10.1145/3695767
Reference28 articles.
1. Yubo Chen Liheng Xu Kang Liu Daojian Zeng and Jun Zhao. 2015. 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 of the Asian Federation of Natural Language Processing ACL 2015 July 26-31 2015 Beijing China Volume 1: Long Papers. The Association for Computer Linguistics 167–176. https://doi.org/10.3115/v1/p15-1017
2. Yubo Chen Liheng Xu Kang Liu Daojian Zeng and Jun Zhao. 2015. 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 of the Asian Federation of Natural Language Processing ACL 2015 July 26-31 2015 Beijing China Volume 1: Long Papers. The Association for Computer Linguistics 167–176. https://doi.org/10.3115/V1/P15-1017
3. Event Extraction by Answering (Almost) Natural Questions
4. Event Extraction by Answering (Almost) Natural Questions
5. Cuiyun Han, Jinchuan Zhang, Xinyu Li, Guojin Xu, Weihua Peng, and Zengfeng Zeng. 2022. DuEE-Fin: A Large-Scale Dataset for Document-Level Event Extraction. In Natural Language Processing and Chinese Computing. Springer International Publishing, 172–183.
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