# Heterogeneous Graph Neural Network for Short Text Classification
## 4 Experiment
### Ablation Experiment  
In this paper, ablation experiments of small images and various heterogeneous images are carried out for the three proposed composition methods. As shown in Table 3 and Figure 4, the experiments are divided into three categories:  
* Take the W-W, W-P, T-W maps as the composition, input the GCN model, and predict the text.
* Combining three small graphs in pairs to form a heterogeneous graph as input for prediction.
* The TWPGCN model proposed in this paper.  
Through the ablation experiment, it can be found that the result of the heterogeneous graph combined is better than that of the small graph in general, because the addition of more nodes and edges improves the information content of the graph. From the experimental results of group a, it seems that inputting W-W graph for the model has better  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Group** & **Model** & **20 NG** & **R8** & **R52** & **Ohsumed** & **MR** \\ \hline \multirow{4}{*}{d} & GCN-C & \(0.8142\pm 0.0032\) & \(0.9699\pm 0.0012\) & \(0.9275\pm 0.0022\) & \(0.6386\pm 0.0053\) & \(0.7722\pm 0.0027\) \\  & GCN-S & - & \(0.9680\pm 0.0020\) & \(0.9274\pm 0.0024\) & \(0.6282\pm 0.0037\) & \(0.7699\pm 0.0014\) \\  & GCN-F & - & \(0.9689\pm 0.0006\) & \(0.9320\pm 0.0004\) & \(0.6304\pm 0.0077\) & \(0.7674\pm 0.0021\) \\  & Text GCN & \(0.8634\pm 0.0009\) & \(0.9707\pm 0.0010\) & \(0.9356\pm 0.0018\) & \(0.6836\pm 0.0056\) & \(0.7674\pm 0.0020\) \\  & SHINE & - & - & - & \(0.4557\) & \(0.6458\) \\  & TWPGCN & \(0.8764\pm 0.0012\) & \(0.9794\pm 0.0007\) & \(0.9421\pm 0.0032\) & \(0.7030\pm 0.0021\) & \(0.7752\pm 0.0018\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 4: Efficiency comparison experiment.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Group** & **Model** & **20 NG** & **R8** & **R52** & **Ohsumed** & **MR** \\ \hline \multirow{4}{*}{a} & W-W & 0.8671 & 0.9772 & 0.9350 & 0.6760 & 0.7721 \\  & W-P & 0.8573 & 0.9703 & 0.9380 & 0.6777 & 0.7684 \\  & T-W & 0.8455 & 0.9584 & 0.9272 & 0.6688 & 0.7665 \\ \hline \multirow{4}{*}{b} & W-W+T-W & 0.8657 & 0.9689 & 0.9385 & 0.6820 & 0.7696 \\  & W-W+W-P & 0.8655 & 0.9767 & **0.9428** & 0.6962 & 0.7743 \\  & W-P+T-W & 0.8728 & 0.9762 & 0.9326 & 0.6860 & 0.7676 \\ \hline \hline c & TWPGCN & **0.8764** & **0.9794** & 0.9421 & **0.7030** & **0.7752** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation experiment.  
results. It represents the co-occurrence relationship between words in the text, which shows that the recognition of fixed collocations and special words is very important in short text classification. In group b, the experimental effect of the heterogeneous graph composed of W-W and W-P is better. The addition of POS tags provides the attributes of different words for the word co-occurrence graph, which enables the model to have a better ability to distinguish important nodes. TWPGCN integrates both word co-occurrence and label features into text features, and the enhanced word features transmit information to text nodes, which makes the information contained in the text nodes more recognizable and classification clearer. Experimental data show that TWPGCN is the most expressive for short text classification in the four datasets, except R52.