科研成果详情

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
ISBN978-1-57735-809-1
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.

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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
被引频次:48[WOS]   [WOS记录]     [WOS相关记录]
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.
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