Distantly Supervised Relation Extraction using Global Hierarchy Embeddings and Local Probability Constraints

Abstract To find relational facts of interest from plain texts, distantly supervised relation extraction (DSRE) has drawn significant attention. Recent works exploit relation hierarchies to mine more clues for long-tail relations and achieve good performance. However, they ignore or underutilize the correlation of relations in the hierarchical structure. Empirically, the correlation facilitates knowledge transfer between different relations to further handle long-tail relations and improves inter-relational discrimination. In this paper, we devise an end-to-end network to model the correlation of relations from two perspectives. Globally, we construct an undirected connected graph according to the relation hierarchies, and employ Graph Attention Networks (GATs) to aggregate node information and generate correlation-aware Global Hierarchy Embeddings (GHE). Locally, we assume that along the relation hierarchies, the classification results of adjacent levels should be highly interdependent, and introduce a constraint called Local Probability Constraints (LPC) to take it into account. LPC is then combined with a branch network for both sentence-level and bag-level classification. Experimental results on the popular New York Times (NYT) dataset show that, our model GHE-LPC outperforms other state-of-the-art baselines in terms of AUC, Top-N precision, accuracy of Hits@K, etc.