# The Study on the Text Classification Based on Graph Convolutional Network and BiLSTM +
## 4 Discussion
### Ablation Study  
In order to further study the benefits of the various components of IMGCN, this paper conducted ablation experiments on the effectiveness of the constructed model. Since the experimental results of IMGCN and the three variant models on the R8 dataset are not much different, Table 2 only shows the results of the ablation experiments on the three public datasets of SST-2, MR, and CoLA. We selected two indicators, weighted average F1-Score and macro F1-Score. The experimental results of the IMGCN and the three variant models prove that the three components used in this paper can improve the performance of IMGCN. Among them, MGB is a model that only uses point-state mutual information to build a graph network for learning and stitches it together with the local information obtained by BERT. Based on the MGB model, IMB introduces a model that relies on syntactic and semantic dictionaries for composition. IMGCN is a model that further fuses graph features and local features obtained by BERT using hierarchical BiLSTM on the basis of IMB. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**SST-2**} & \multicolumn{2}{c}{**MR**} & \multicolumn{2}{c}{**CoLA**} & \multicolumn{2}{c}{**R8**} \\ \cline{2-9}
**Model** & **Weighted Avg F1** & **Acc** & **Weighted Avg F1** & **Acc** & **Weighted Avg F1** & **Acc** & **Weighted Avg F1** & **Acc** \\  & **(Macro F1)** & **(Macro F1)** & **(Macro F1)** & **(Macro F1)** & **(Macro F1)** & **(Macro F1)** & **(Macro F1)** & **Acc** \\ \hline Text-GCN & 80.45 & 82.39 & 75.67 & 76.70 & 56.18 (52.30) & 52.30 & — & 97.00 \\ Bi-LSTM & 81.32 & 83.50 & 76.39 & 77.70 & 62.88 (55.25) & 64.10 & — & 96.30 \\ VGCN & 81.64 & 83.43 & 76.42 & 76.42 & 63.59 (54.82) & 65.61 & 97.12 (94.65) & 97.43 \\ BERT & 91.49 & 91.53 & 86.24 & 80.30 & 81.22 (77.02) & 81.20 & 98.12 (95.59) & 98.20 \\ STGCN + BERT + & — & — & — & 82.50 & — & — & — & **98.50** \\ BiLSTM & 91.93 & 91.94 & 86.35 & 86.35 & 83.68 (80.46) & 83.70 & 98.04 (95.43) & 98.04 \\ \hline IMGCN & **92.48** & **92.48** & **87.81** & **87.81** & **84.02 (80.91)** & **84.37** & **98.14 (96.50)** & 98.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Accuracy, Weighted average F1-Score and (Macro F1-score) on the test sets. As shown in Table 2, we can see that the performance of the model with the corresponding module added has a certain degree of improvement compared with the overall model, indicating that the modules are complementary to each other. It can be observed that different modules have different functions in different datasets. As shown in Figure 5, the performance of the model named IMB has a certain improvement compared to the MGB model. The F1 score of the IMB model on the three datasets is higher than that of the IGB model. Although the gap is not high, it also proves that the introduction of dependency syntax and semantic dictionary can enrich the word embedding information of the graph. Compared with the F1 score of the IMB model, the F1 scores of the SST-2, MR, and CoLA datasets on the IMGCN model all increased to varying degrees. This shows that the fusion of global information and local information, compared with simple splicing, fusion through model learning has better classification ability, which also shows the effectiveness of this module. After combining the feature representation obtained by GCN and the pre-trained BERT, the feature fusion module is used for further learning, and the classification effect is better than directly splicing the two parts of the feature information, but the increase in the model will affect the training time.