题名 | Multi-labeled relation extraction with attentive capsule network |
作者 | |
发表日期 | 2019 |
会议名称 | 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Annual Conference on Innovative Applications of Artificial Intelligence, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 |
会议录名称 | 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019
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ISBN | 978-1-57735-809-1 |
页码 | 7484-7491 |
会议日期 | JAN 27-FEB 01, 2019 |
会议地点 | Honolulu, HI, USA |
出版者 | AAAI Press |
摘要 | To disclose overlapped multiple relations from a sentence still keeps challenging. Most current works in terms of neural models inconveniently assuming that each sentence is explicitly mapped to a relation label, cannot handle multiple relations properly as the overlapped features of the relations are either ignored or very difficult to identify. To tackle with the new issue, we propose a novel approach for multi-labeled relation extraction with capsule network which acts considerably better than current convolutional or recurrent net in identifying the highly overlapped relations within an individual sentence. To better cluster the features and precisely extract the relations, we further devise attention-based routing algorithm and sliding-margin loss function, and embed them into our capsule network. The experimental results show that the proposed approach can indeed extract the highly overlapped features and achieve significant performance improvement for relation extraction comparing to the state-of-the-art works. © 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. |
URL | 查看来源 |
收录类别 | CPCI-S |
语种 | 英语English |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000486572502003 |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | https://repository.uic.edu.cn/handle/39GCC9TT/4479 |
专题 | 个人在本单位外知识产出 |
作者单位 | 1.Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China 2.State Key Lab of IoT for Smart City, University of Macau, 999078, Macau, China |
推荐引用方式 GB/T 7714 | Zhang, Xinsong,Li, Pengshuai,Jia, Weijiaet al. Multi-labeled relation extraction with attentive capsule network[C]: AAAI Press, 2019: 7484-7491. |
条目包含的文件 | 条目无相关文件。 |
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