# Modeling Semantic-Aware Prompt-Based Argument Extractor in Documents
## 1 Introduction
This model not only tackles these challenges but also enhances the practical utility of event extraction in large-scale applications such as news analysis, public opinion discovery, and intelligence gathering. The SPARE model integrates entity extraction, heterogeneous graph construction, event type detection, and argument filling based on table generation, aiming to achieve efficient event argument extraction. Firstly, it encodes the context through Conditional Random Fields (CRFs), which identifies entities in the text and provides the basis for subsequent modules. CRFs can capture the dependencies between contexts by modeling global features, thereby enhancing the accuracy and coherence of entity recognition. The model then constructs a heterogeneous graph with documents, sentences, and entities as nodes, and captures long-distance dependencies and event co-occurrence information through graph convolutional networks (GCNs), generating a document-level global representation. Using this global representation, the model accurately identifies event types, effectively addressing the problem of event co-occurrence. Moreover, the model innovatively introduces a semantic role attention mechanism based on position-awareness, strengthening the semantic association between trigger words and arguments. Finally, through span selection, the model accurately extracts event arguments from the text and fills them into predefined argument slots, achieving end-to-end event information structuring extraction. This design can efficiently handle long-distance dependencies and complex event co-occurrence situations. Our experimental evaluations on the RAMS and WikiEvents datasets reveal a statistically significant improvement in F1 scores. These results validate the excellent performance of the proposed method in the event extraction task. Our contributions can be summarized as follows:  
* To address the challenges of long-distance dependencies, a document-sentence-entity heterogeneous graph is constructed and graph convolutional networks (GCNs) are employed to model global semantic associations. This approach effectively captures interactions between cross-sentence triggers and arguments, enabling the model to better handle information dispersed across different sentences and paragraphs. * To tackle the challenges of event co-occurrence, a position-aware semantic role (SRL) attention mechanism is proposed. This mechanism strengthens the association between semantic and positional information, thereby improving the accuracy of argument extraction and allowing the model to more effectively handle the complex relationships between multiple events in a document. * We conducted comprehensive evaluations of SPARE on two widely recognized benchmark datasets in the field of event argument extraction: RAMS and WikiEvents. The experimental outcome is that SPARE surpasses the latest baseline methods. The remainder of this paper is structured as follows: Section 2 presents a comprehensive literature review of event type recognition, document-level event argument extraction, and joint event extraction. Section 3 outlines the task definition and introduces two proposed models: a model for dynamic recognition of event types based on graph neural networks and an event argument extraction model based on table generation. Section 4 describes the datasets and evaluation metrics utilized in the study, details the experimental parameterization, compares the proposed models with baseline approaches, presents the main experimental results, and includes ablation experiments to analyze the contributions of different components. Section 5 provides a detailed cross-event correlation analysis and evaluates the model's semantic capture capabilities in both inter-event and intra-event correlation perspectives. Section 6 presents case studies to further illustrate the practical applications and effectiveness of the proposed models. Finally, Section 7 summarizes the key findings of this study and offers concluding remarks.