Relation Classification via Multi-Level Attention CNNs
Linlin Wang, Zhu Cao, Gerard de Melo, Zhiyuan Liu · 2016
Relation classification is a crucial ingredient in numerous information extraction systems seeking to mine structured facts from text.We propose a novel convolutional neural network architecture for this task, relying on two levels of attention in order to better discern patterns in heterogeneous contexts.This architecture enables endto-end learning from task-specific labeled data, forgoing the need for external knowledge such as explicit dependency structures.Experiments show that our model outperforms previous state-of-the-art methods, including those relying on much richer forms of prior knowledge.