HIORE: Leveraging High-order Interactions for Unified Entity Relation
  Extraction
Yijun Wang Changzhi Sun Yuanbin Wu Lei Li Junchi Yan Hao Zhou
Entity relation extraction consists of two sub-tasks: entity recognition and
relation extraction. Existing methods either tackle these two tasks separately
or unify them with word-by-word interactions. In this paper, we propose HIORE,
a new method for unified entity relation extraction. The key insight is to
leverage the high-order interactions, i.e., the complex association among word
pairs, which contains richer information than the first-order word-by-word
interactions. For this purpose, we first devise a W-shape DNN (WNet) to capture
coarse-level high-order connections. Then, we build a heuristic high-order
graph and further calibrate the representations with a graph neural network
(GNN). Experiments on three benchmarks (ACE04, ACE05, SciERC) show that HIORE
achieves the state-of-the-art performance on relation extraction and an
improvement of 1.1~1.8 F1 points over the prior best unified model.