## 3 Method Description
### Encoding Layer
#### 3.1.3 Syntactic Structural Module  
This study employed the dependency parsing techniques to deeply analyze sentence structures. First, a syntactic dependency tree was constructed and stored using a depen  
Figure 2: Self-attention mechanism architecture diagram.  
dency matrix to facilitate sentence structure analysis. Then, a GCN was applied to extract connections between adjacent nodes in the graph, obtaining a text feature representation that incorporated syntactic structures.  
(1) Dependency Parsing. We constructed a dependency matrix to capture the sentences' structural information, enhancing the model's deep understanding of the corpus. First, the natural language processing tool SpaCy was used to tokenize each text and construct the corresponding dependency parse tree [35; 36]. For an input sentence \(S=\{w_{1},w_{2},...,w_{L}\}\) of length \(L\), an \(L\times L\) adjacency matrix \(A\) was constructed. For example, the dependency parsing result of the sentence \"Cyahlofop butyl is a herbicide for controlling barnyard grass and Polygonum in rice fields\" is shown in Figure 3a. In this example, \"is\" is the dependency tree's root node, representing the main action of the sentence; \"compound\" indicates that the relationship between \"cylalofop\" and \"butyl\" is a compound noun relationship; \"dobj\" indicates that \"barnyard grass\" is the direct object of the action \"controlling\"; and \"attr\" indicates that \"herbicide\" is an attribute of \"cylalofop butyl\", acting as the predicate nominative of \"is\". Next, based on the dependency parsing results, a dependency matrix was constructed: if two words had a dependency relationship, their adjacency was set to \"1\"; otherwise, it was set to \"0\". Additionally, undirected edges and self-loops were added to strengthen both the connections between the nodes and the nodes themselves. The final dependency matrix is illustrated in Figure 3b.  
(2) Feature Extraction with a GCN for Graph Structures [37; 38; 39; 40]: By constructing a syntactic dependency graph, redundant information between tokens could be ignored, and information features from distantly related tokens could be integrated to address the problem of large spans between entities. Typically, a graph structure consisted of a set of nodes \(V\) and a set of edges \(E\). Each token corresponded to a node in \(V\), while the edge set \(E\) was formed by the dependency relations between tokens, as defined in the adjacency matrix.  
Figure 3: Syntactic dependency matrix. In the Figure 3, (**a**) is the result of semantic analysis of example sentences. (**b**) is a semantic adjacency matrix constructed from (**a**).  
For each node in the adjacency graph, the GCN updated the corresponding embedded node by aggregating the features of its neighboring nodes.  
In an N-layer GCN, the hidden vector \(h_{i}^{(n-1)}\) of the \(j\)-th neighbor of the \(i\)-th node from the \((n-1)\)-th layer was used as the input. When \(n=0\), \(h_{i}^{(0)}\) represents the initial hidden vector of the \(i\)-th node before the graph convolution operation, corresponding to the node vector \(H\) output based on the self-attention layer. A graph convolution operation was defined in the manner shown in Equation (3).  
\[h_{i}^{(n)}=\sigma\Bigg{(}\sum_{j=1}^{\tilde{n}}A_{ij}W^{(n)}h_{j}^{n-1}+b^{(n )}\Bigg{)} \tag{3}\]  
In Equation (3), \(\sigma\) is the activation function ReLU, \(A_{ij}\) is the dependency relationship between nodes \(i\) and \(j\), \(W^{(n)}\) is the linear transformation, and \(b^{(n)}\) is the bias. During each graph convolution computation, each node aggregated the feature information of its neighboring nodes.  
Finally, the output \(h_{i}\) from the GCN was concatenated with the multi-head attention output \(H_{i}\) to obtain the final feature vector \(e_{i}^{f}\), which integrated both semantic and syntactic information, as shown in Equation (4). Here, \(W_{f}\) is the linear transformation matrix, and \(b_{f}\) is the bias.  
\[e_{i}^{f}=W_{f}[H_{i};h_{i}]+b_{f} \tag{4}\]