# Heterogeneous Graph-Convolution-Network-Based Short-Text Classification
## 4 Experiment and Performance Analysis
### Ablation Experiment  
In order to verify the effectiveness of each part of this model, we conducted an ablation experiment. The experimental results are shown in Table 6, where w/o represents \"not included\". The experimental settings are described as follows:  
W/o BERT: This indicates that the BERT pre-training model is not used for feature fusion. Further, in w/o BERT, entity information is introduced to establish a text-entity-word heterogeneous graph to learn the representation of text nodes for short-text classification. W/o Entity: w/o Entity indicates that no entity knowledge is introduced to expand short text, and feature fusion is performed on the document nodes and the word nodes learned from graph convolution networks through BERT. W/o GCN: w/o GCN indicates that the model only encodes the input information through BERT and then outputs the text classification result. According to Table 6, the results on the AGNews dataset indicate that the accuracy of the model without GCN can only reach 86.75%, and the accuracy of the model without BERT can only reach 88.12%. The results on the R52 dataset indicate that the accuracy of the model without GCN can only reach 91.12%, and the accuracy of the model without BERT can only reach 93.87%. These results are obviously lower than those for SHGCN. If the relationship between documents and words is not modeled, the accuracy of the model is \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **AGNews** & **R52** & **MR** \\ \hline SHGCN & 88.38 & 93.87 & 82.87 \\ w/o BERT & 88.12 & 93.61 & 75.63 \\ w/o Entity & 86.31 & 93.75 & 81.03 \\ w/o BERT w/o Entity & 86.75 & 93.56 & 76.74 \\ w/o GCN & 86.75 & 91.12 & 81.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of accuracy under different models. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**AGNews**} & \multicolumn{2}{c}{**R52**} & \multicolumn{2}{c}{**MR**} & \multirow{2}{*}{**Mean Value**} \\ \cline{2-2} \cline{6-7}  & \multirow{2}{*}{**Precision**} & \multicolumn{1}{c}{\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} } & \multirow{2}{*}{**Precision**} & \multicolumn{1}{c}{\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} } & \multirow{2}{*}{**Precision**} & \multicolumn{1}{c}{\
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} } & \multirow{2}{*}{**Precision**} & \multicolumn{1}{c}{
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} } & \multirow{2}{*}{**Mean Value**} \\ \hline TF-IDF + LR & 87.56 & 0.36 & 88.57 & 0.75 & 74.34 & 0.90 & 83.62 \\ CNN & 86.06 & 0.20 & 90.08 & 0.71 & 73.97 & 0.27 & 83.37 \\ BiLSTM & 82.85 & 0.79 & 85.44 & 0.75 & 72.67 & 0.65 & 80.32 \\ fastText & 77.47 & 1.70 & 90.26 & 0.32 & 69.50 & 1.20 & 79.08 \\ TextGCN & 79.09 & 7.79 & 90.62 & 0.67 & 76.55 & 0.16 & 82.09 \\ TensorGCN & 86.77 & 0.85 & 92.68 & 0.38 & 77.83 & 0.40 & 85.76 \\ SHGCN & 87.80 & 0.94 & 91.99 & 1.19 & 83.85 & 2.87 & 87.88 \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of precision under different models.