HIORE: Leveraging High-order Interactions for Unified Entity Relation Extraction
This work addresses the problem of extracting entities and their relations from text for natural language processing applications, representing an incremental advance with specific performance gains.
The paper tackles unified entity relation extraction by leveraging high-order interactions among word pairs, achieving state-of-the-art performance on relation extraction and improving F1 scores by 1.1 to 1.8 points over prior unified models.
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.