RTF: Region-based Table Filling Method for Relational Triple Extraction

• URL: http://arxiv.org/abs/2404.19154v2
• Date: Thu, 13 Jun 2024 16:26:15 GMT
• Title: RTF: Region-based Table Filling Method for Relational Triple Extraction
• Authors: Ning An, Lei Hei, Yong Jiang, Weiping Meng, Jingjing Hu, Boran Huang, Feiliang Ren,
• Abstract summary: We propose a novel Region-based Table Filling method (RT) for extracting triples from knowledge graphs. We devise a novel regionbased tagging scheme and bi-directional decoding strategy, which regard each triple as a region on the relation-specific table, and identifies triples by determining two endpoints of each region. Experimental results show our method achieves better generalization capability on three variants of two widely used benchmark datasets.
• Score: 17.267920424291372
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Relational triple extraction is crucial work for the automatic construction of knowledge graphs. Existing methods only construct shallow representations from a token or token pair-level. However, previous works ignore local spatial dependencies of relational triples, resulting in a weakness of entity pair boundary detection. To tackle this problem, we propose a novel Region-based Table Filling method (RTF). We devise a novel region-based tagging scheme and bi-directional decoding strategy, which regard each relational triple as a region on the relation-specific table, and identifies triples by determining two endpoints of each region. We also introduce convolution to construct region-level table representations from a spatial perspective which makes triples easier to be captured. In addition, we share partial tagging scores among different relations to improve learning efficiency of relation classifier. Experimental results show that our method achieves state-of-the-art with better generalization capability on three variants of two widely used benchmark datasets.

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