## 4 Experiment and Analysis
### Experiment Setup and Results
#### 4.2.3 Model Comparison  
Finally, this section experimentally compares the performance differences between some baseline models and current mainstream models with the DIF-A-GCN biomedical relation extraction model proposed in this paper. For the DDI dataset, a number of CNN-based and RNN-based baseline models are selected in this paper. A number of them are dependency-based models. The more typical ones are: the syntactic CNN (SCNN) proposed by [PERSON] et al. (2016) [24], which used syntactic information of sentences as well as features of lexical labels and dependency trees for syntactic word embedding. [PERSON] et al. (2016) [25], which used multi-channel CNN (MCCNN) for DDI relation extraction and achieved the fusion of multi-word embeddings. Joint AB-LSTM, proposed by [PERSON] and [PERSON] (2018) [26], consists of two RNN-based models concatenated with the aim of taking advantage of maximum pooling and attention \begin{table}
\begin{tabular}{c c c} \hline \hline  & **DDI** & **CIVIC** \\ \hline
1-layer GCN & 74.0 & 75.8 \\
2-layer GCN & **77.1** & **77.9** \\
3-layer GCN & 73.5 & 75.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of F1 values (%) of experimental results. \begin{table}
\begin{tabular}{c c} \hline \hline \multirow{2}{*}{DDI Sentence} & In a small combination study of **ARAVA** with **methotrexate**, a 2- to \\  & 3-fold elevation in liver enzymes was seen in 5 of 30 patients. \\ \hline True relation & e1: ARAVA, e2: methotrexate, RE: effect \\ \hline Full & e1: ARAVA, e2: methotrexate, RE: mechanism \\ \hline SDP & e1: ARAVA, e2: methotrexate, RE: mechanism \\ \hline L+G(ours) & e1: ARAVA, e2: methotrexate, RE: effect \\ \hline \hline \end{tabular}
\end{table} 
Table 3: DDI data case study. pooling techniques using two independent modules, each with a Bi-LSTM network and applying two pooling techniques to obtain features. [PERSON] et al. (2018) [27] proposed a model of RvNN based on bottom-up and top-down tree structures. The core idea of the method is to enhance the high-level representation of tree nodes by following the recursion of the propagation structure on different branches in the tree. [PERSON] et al. (2017) [28] proposed a new neural sequence model with a position-aware attention mechanism for LSTM networks for relation extraction. The sequence model works well on short-range relations compared to methods using dependency information. [PERSON] et al. (2020) [29] proposed a BERE model using a hybrid encoding network to represent each sentence semantically and syntactically, and a feature aggregation network for prediction after considering all relevant utterances, which performs well on the DDI dataset. We selected the current mainstream relation extraction model mentioned above to compare with the model method proposed in this paper. The experimental results of the DDI dataset are shown in Table 5:  
For the CIVIC dataset, this paper selects some mainstream models for biomedical relation extraction from the perspective of dependency syntax tree. Tree-LSTM: the authors firstly proposed to use a bidirectional tree LSTM to capture the dependency structure of the target sentences, making the features imply syntactic structure features. Graph LSTM-FULL [30]: the authors explored a graph long short-term memory network-based framework for relation extraction. It combines intra-sentence and inter-sentence dependencies, such as order, syntax, and discourse relations, to learn a robust context representation for entities as input to a relation classifier. AGGCN [31]: an attention-guided graph convolutional network for relation extraction was proposed to learn soft pruning in an end-to-end strategy model. Table 6 shows the experimental results of this paper's model compared with the above more mainstream model methods on the CIVIC dataset. The experimental results in Tables 5 and 6 demonstrate the effectiveness of our model. First, for DDI dataset, the RNN-based model works better than the CNN-based model.