Modeling Semantic-Aware Prompt-Based Argument Extractor in Documents

Event extraction aims to identify and structure event information from unstructured text, playing a critical role in real-world applications such as news analysis, public opinion discovery, and intelligence gathering. Traditional approaches, however, struggle with event co-occurrence and long-distance dependencies. To address these challenges, we introduce the Semantic-aware Prompt-based Argument Extractor (SPARE) model, which integrates entity extraction, heterogeneous graph construction, event type detection, and argument filling. By constructing a document–sentence–entity heterogeneous graph and employing graph convolutional networks (GCNs), the model effectively captures global semantic associations and interactions between cross-sentence triggers and arguments. Additionally, a position-aware semantic role (SRL) attention mechanism is proposed to enhance the association between semantic and positional information, improving argument extraction accuracy in the context of event co-occurrence. The experimental outcomes on the Richly Annotated Multilingual Schema-guided Event Structure (RAMS) and WikiEvents datasets display considerable F1 score improvements, which confirms the model’s effectiveness.