## 4 Experiment and Analysis
### Experiment Setup and Results
#### 4.2.1 Verification of Pruning Strategy  
In this paper, we first verify the correctness of the pruning strategy. We start from the dependency structure. In order to construct the graph of the bidirectional GCN, we process each input sentence using Stanford CoreNLP, a Stanford syntactic parser, to obtain lexical information and the dependency syntax tree of the sentence. After obtaining the dependency tree of each sentence, we perform dependency analysis on the full dependency tree (Full), SDP (shortest dependency path) dependency tree and the local-global connected dependency tree (L+G) proposed in this paper to obtain the dependency path adjacency matrix and dependency type matrix of different state dependency trees. Experiments are done to verify the correctness and advantages of the pruning strategy proposed in this paper, and the following results are obtained on two biomedical datasets:  
In Table 2, Full represents the complete dependency tree, SDP represents the current mainstream pruning method, and L+G represents our proposed pruning method. The experimental results in Table 2 show that when the input is a complete dependency syntax tree, the model is less effective than other pruned strategies. It is mainly caused by too much external information that makes the model prediction confusing. Especially in biomedical dataset, when the sentence length is long, the semantic is complex, and there are many dependencies between words, which has a improper semantic guidance when predicting the relation between two given entities. SDP is used by many researchers by preserving the dependency information on the shortest path between two entities. It ignores the contextual dependency information within the entity associated with the entity, resulting in the effect of over-pruning and missing information. The pruning strategy in this paper fully considers the intra-entity and inter-entity dependency information and discards the dependency connections between words that are not related to entities. There is a significant improvement on both datasets, which proves that the pruning strategy we propose is reasonable and effective.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Parameter Value** \\ \hline Epoch & 100 \\ BatchSize & 16 \\ Learning\_rate & \(3\times 10^{-5}\) \\ Warmup\_rate & 0.06 \\ Random\_seed & 42 \\ Max\_seq\_length & 128 \\ Dropout & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter settings.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **DDI** & **CIVIC** \\ \hline Full & 73.3 & 74.2 \\ SDP & 74.7 & 76.1 \\ L+G(ours) & **77.1** & **77.9** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of F1 values (%) of experimental results.  
Table 3 verifies the effectiveness of the pruning strategy in this paper through one data of DDI. The main meaning of the sentence is about the 2- to 3-fold elevation of liver enzymes in 5 out of 30 patients in the combination study of ARAVA with methotrexate. It represents the effect produced by the interaction of the two drugs and does not describe the drug mechanism, so the relation is effect rather than mechanism. Whether the full dependency tree or the SDP pruned dependency tree fails to extract the correct relation, they both subject the model to the dependency information for the term liver enzymes, incorrectly directing the model to believe that this sentence represents the pharmacological mechanism between the two drug entities. The pruning strategy in this paper removes the noisy information and directs the model to think that these are the two drugs that will have an effect on liver enzymes. The model will follow this layer of semantics to predict the relation between the entity pairs without being influenced by the liver enzymes words.