As can be seen from the data in the table, PEGCN has the highest
classification accuracy in each dataset when λ is 0.3, that is,
when the weight ratio of GCN to BERT is 0.3/0.7. The accuracy of the
four datasets is 98.22%, 96.65%, 73.26%, and 89.59%, respectively.
Compared with the case of a λ- value of 0.5, the accuracy is
0.04%,1.63%, 0.96%, and 2.08% higher, so λ is set to 0.3
herein because the large-scale pre-training model can significantly
improve the classification effect, and assigning a larger weight is
conducive to improving the accuracy. For specific datasets, GCN is also
required for further feature extraction, so the best effect arises when
the weight ratio is 0.3/0.7.
Based on TextGCN, the proposed method delivers an improvement. A
position graph convolutional network (PGCN) is a GCN model that adds
position information. A position and Bert graph convolutional network
(PBGCN) is a network model that combines BERT based on the addition of
position information. Meanwhile, experiments on GAT were conducted to
verify the effectiveness of the proposed method. A position and Bert
graph attention network (PBGAT) is a GAT network with position
information and a model trained together with BERT. Finally, the method
of PEGCN is GCN model using position information and edge features. The
improved experimental results are listed in Table 3.
Table 3. Module validity experiment accuracy comparison. metric:
accuracy (%)