HIORE: Leveraging High-order Interactions for Unified Entity Relation Extraction

• URL: http://arxiv.org/abs/2305.04297v1
• Date: Sun, 7 May 2023 14:57:42 GMT
• Title: HIORE: Leveraging High-order Interactions for Unified Entity Relation Extraction
• Authors: Yijun Wang, Changzhi Sun, Yuanbin Wu, Lei Li, Junchi Yan, and Hao Zhou
• Abstract summary: We propose HIORE, a new method for unified entity relation extraction. The key insight is to leverage the complex association among word pairs, which contains richer information than the first-order word-by-word interactions. Experiments show that HIORE achieves the state-of-the-art performance on relation extraction and an improvement of 1.11.8 F1 points over the prior best unified model.
• Score: 85.80317530027212
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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