Multi-Labeled Relation Extraction with Attentive Capsule Network


  • Xinsong Zhang Shanghai Jiao Tong University
  • Pengshuai Li Shanghai Jiao Tong University
  • Weijia Jia University of Macau & Shanghai Jiao Tong University
  • Hai Zhao Shanghai Jiao Tong University



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.




How to Cite

Zhang, X., Li, P., Jia, W., & Zhao, H. (2019). Multi-Labeled Relation Extraction with Attentive Capsule Network. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 7484-7491.



AAAI Technical Track: Natural Language Processing