Tensor Graph Convolutional Networks for Text Classification
Table 4: Analysis of the effectiveness of SeqGraph, SynGraph, and SemGraph. In the upper half table, the first three rows are the results of only using a single graph, while the lower half table is the results of ablation experiments. For example, SemGraph(w/o) means TensorGCN without using SemGraph. Graph tensor means three graphs are used.
['[BOLD]  Model', '[BOLD] 20NG', '[BOLD] R8', '[BOLD] R52', '[BOLD] Ohsumed', '[BOLD] MR']
We also examine and perform an analysis of the effectiveness of our constructed text graph tensor. We notice that graph tensor has the best performance, and each pair of graphs always show better performance than any single graph. The phenomenons illustrate that the proposed three kinds of text graphs are complementary to each other. We also observed that different text graphs play different roles in different datasets. For example, in the ablation experiments, there are worse accuracy results on the Ohsumed dataset and MR dataset without using SynGraph. It means that syntactic dependency plays an important role in those two datasets.