Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction
Ningyu Zhang | Shumin Deng | Zhanling Sun | Xi Chen | Wei Zhang | Huajun Chen
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
A capsule is a group of neurons, whose activity vector represents the instantiation parameters of a specific type of entity. In this paper, we explore the capsule networks used for relation extraction in a multi-instance multi-label learning framework and propose a novel neural approach based on capsule networks with attention mechanisms. We evaluate our method with different benchmarks, and it is demonstrated that our method improves the precision of the predicted relations. Particularly, we show that capsule networks improve multiple entity pairs relation extraction.