# The Study on the Text Classification Based on Graph Convolutional Network and BiLSTM +
## 4 Discussion
### Main Experimental Results  
To verify the performance of the proposed model, we conduct comparative experiments with other models. Including the classic Bi-LSTM [35], Text-GCN [13], and BERT [10] models, and three improved GCN methods: VGCN [15], VGCN-BERT [15], and STGCN + BERT + BiLSTM [14].  
* Bi-LSTM: Bidirectional LSTM is often used for text classification. Use BERT pre-trained word embeddings as input to the Bi-LSTM model.
* Text-GCN: A text classification method that uses graphs to model text. The words and documents in the text are regarded as nodes, where the edges of the document and the words are based on the appearance information of the words in the document, and the edges of the words and the words are based on the global word co-occurrence information of the words. It has many improvements over many of the most advanced models.
* VGCN: The model only uses VGCN. The pre-trained word embedding of BERT is used as an input. The output of VGCN is relayed to a fully connected layer with the Softmax function to generate classification scores. This model only uses the global information of the vocabulary graph.
* BERT: We use a small version (BERT-base-uncase) of pre-trained BERT.
* VGCN-BERT: Based on the word co-occurrence information, a graph convolutional network is constructed on the vocabulary graph, and then the graph embedding and word embedding are provided to the self-attention encoder in BERT.
* STGCN + BERT + BiLSTM: The topic model is employed to extract the short text topic information, and a short-text graph is constructed by word co-occurrence and document word relations. The word nodes and document nodes trained by the GCN and vectors generated by the BERT's hidden layer are fed together to the BiLSTM classifier for short text classification.  
In order to comprehensively evaluate the model, the weighted average F1-Score and macro F1-Score are selected as evaluation indicators. The main results on the accuracy,weighted average F1-Score, and macro F1-Score on test sets are presented in Table 1. The experimental results on four benchmark datasets confirm that the performance of IMGCN is basically better than other baseline models, which further proves the effectiveness and robustness of IMGCN in text classification. The \"--\" in the table represents no data. The bold in the table indicates the best results of the model. The Macro F1 score is the arithmetic average of multiple F1 scores, and it assigns equal weight to each class. The weighted avg F1 score assigns different weights to different classes according to the number of samples in each class. When calculating the two scores, the respective Macro F1 scores and Weighted avg F1 scores of the SST-2 and MR datasets are close. Since we rounded our calculations to two decimal places, this resulted in the macro F1 score and the weighted average F1 score being the same on SST-2 and MR.  
It can be seen from the table that IMGCN has a good classification effect compared with the baseline models, which proves the effectiveness of the model in this paper. The performance of the Bi-LSTM model and the models VGCN and Text-GCN that utilize the vocabulary graph are comparable. Compared with the VGCN and BERT models, the model in this paper has a certain degree of effect improvement, indicating that the introduction of dependencies, semantic dictionaries, the BERT model, Bi-LSTM model, and residual attention in this model does enrich the contextual semantic information and local feature information of GCN, making the model in this paper compared to other models have better classification performance. At the same time, compared with VGCN-BERT, IMGCN always performs better. All this shows that the combination of each component of this paper is useful.