# Modeling Semantic-Aware Prompt-Based Argument Extractor in Documents
## 3 Approach
### Event Argument Extraction Model Based on Table Generation
#### 3.3.3 Feature Fusion  
The structure-aware attention mechanism and the cross-attention mechanism are used for iterative representation when decoding the slotted table, and the semantic role  
Figure 3: Event argument extraction model, mainly divided into context encoding (Section 3.3.1), prompt for extraction (Section 3.3.2), feature fusion (Section 3.3.3), and span selection (Section 3.3.4).  
label (SRL) is used for other decoding. Semantic role labeling (SRL) is a natural language processing task. Its core task is to classify semantic roles (e.g., agent, patient, temporal adjunct, locative adjunct) for relevant phrases based on predicates within sentences. In this study, semantic roles are delineated with the help of SRL to distinguish the different components of a sentence and to allow positional attention in these components to be passed between the different semantic roles.  
Take the sentence \"A man injured a girl\" as an example of semantic role labeling analysis, marking semantic roles and dependencies; the specific annotation process is illustrated in Figure 4. To further enhance model performance, after determining the core subject and predicate, the model needs to learn additional location-related semantic information. Based on this, this paper proposes a theoretical assumption: within a specific distance range, the influence of semantic roles on the hidden layer dimension follows a Gaussian distribution. \(Kernel(u)\) is a position-awareness Gaussian kernel function used to quantify the propagation of semantic role influence. Its mathematical definition is as follows:  
\[Kernel(u)=\exp\left(\frac{-u^{2}}{2\sigma^{2}}\right) \tag{17}\]  
We construct an influence basis matrix \(K\) based on this assumption. The matrix \(K\) is defined mathematically as follows:  
\[K(i,u)\sim N(Kernel(u),\sigma) \tag{18}\]  
where \(i\) represents the dimension, \(u\) represents the semantic role distance, and \(K(i,u)\) represents the influence value. Additionally, we introduce a normal distribution \(N\).  
By analyzing the positional relationships of semantic roles, we construct an influence matrix for each role and derive context-aware impact vectors through cumulative distance-based statistics. The cumulative impact vector \(S_{r_{j}}\) of semantic roles at position \(j\) is finally obtained:  
\[S_{r_{j}}=KC_{j} \tag{19}\]  
where \(C_{j}\) represents the distance statistic vector for core semantic roles at \(j\). The vector \(C_{j}(u)\) is computed by summing the indicator functions for positions \(j-u\) and \(j+u\) across all words \(w\) in the sentence.  
\[C_{j}(u)=\sum_{w\in S}[1\text{ else }0\text{ if }(j-u)\in\text{pos}(w)]+[1\text{ else }0 \text{ if }(j+u)\in\text{pos}(w)] \tag{20}\]  
where \(\text{pos}(w)\) denotes the positions of \(w\) in core semantic roles.  
Finally, the obtained influence vectors are combined and multiplied with the hidden layer vectors of RoBERTa-large, which effectively improves the model's understanding of the relationships between semantic roles.  
Figure 4: Semantic role labeling and dependencies: ROOT (main verb or core predicate), HED (action-expressing predicate), SBV (action performer), VOB (action target), ATT (modifier providing additional information).