Tabular affiliation extraction based on graph convolutional network

This paper studies the problem of extracting affiliation relationships between cells in a table in the field of table recognition and analysis. The task of extracting affiliation relationships between tables is defined. Combining the similarity between table and graph structure, a graph representation method for cells in a table is given, and a graph convolutional network-based affiliation relationshipextraction model is proposed. The model aggregates features of cells in a table and their neighboring cells through a graph convolutional network, predicts whether there is an affiliation relationship between cells, and realizes relationship extraction. In order to verify the effectiveness of the model, two datasets, Rel-forms for Chinese forms and Rel-SciTSR for English forms, are annotated. Through experiments, the F1 scores on the above two datasets and the joint dataset reached , respectively 98.61%、96.55%、97.05%, verifying the effectiveness of the affiliation relationship extraction model on these two datasets, and analyzing the influence of different factors such as text content, coordinate information, cell attributes, and relative direction between cells on the experimental results of affiliation relationship extraction.