| Citation: | ZHANG Y T，LI Q Y，LIU S K. Tabular subordination relation extraction based on graph convolutional networks[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（4）：1308-1315 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0382
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This study addresses the lack of research on extracting subordination relations between the cells in a table in the field of table recognition and analysis. It defines the table subordination relation extraction task. Based on the similarity between table and graph structures and the graph representation method for table cells, a subordination relation extraction model is proposed using graph convolutional networks(GCN). The proposed model predicts the subordination relation between cells by aggregating the features of table cells and their adjacent cells through the GCN, and then realizes relation extraction. To verify the effectiveness of the proposed model, two datasets, Chinese Rel-forms and English Rel-SciTSR, were annotated. The experiments achieve 98.61%, 96.55% and 97.05%
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