# Modeling Semantic-Aware Prompt-Based Argument Extractor in Documents
## 1 Introduction  
Event extraction (EE) [1] focuses on extracting events of interest to users from unstructured text, often including identifying event trigger words, event types, and arguments such as participants, time, and place in the event, and presenting them in a structured form. Event extraction involves two core subtasks: event detection and event argument extraction (EAE). The former aims to identifying events contained in the text, while the latter focuses on identifying the arguments involved in these events and their roles. In event argument extraction, arguments refer to both entities and non-entity components of an event, such as time and location, while argument roles define their semantic relationship with event triggers. In the example in Figure 1, in a \"life.die\" event, the trigger word \"killed\" links to arguments like \"killer\"-\"a man\" and \"victims\"-\"people\". Similarly, in a \"life.injure\" event, the trigger word \"injured\" corresponds to the argument-role pair \"victim\"-\"girl\" and \"injurer\"-\"a man\". Event argument extraction has a wide range of applications, from recommendation systems [2] and financial intelligence [3, 4] to question-answering systems [5, 6, 7]. At the sentence level [8, 9], the task focuses on identifying arguments within individual sentences. At the document level [10, 11], however, it requires analyzing multiple sentences to uncover argument relationships and shared contexts. Oneof the major challenges in document-level extraction is event co-occurrence when multiple related or independent events appear in the same text. Effectively addressing this phenomenon is crucial for improving extraction accuracy and efficiency, as it not only enhances the generalization ability of the model but also helps uncover deeper semantic connections between events. Consider a scenario where Event 1 (\"killed\") and Event 2 (\"injured\") share the same subject, \"a man\", suggesting a temporal link between them. Additionally, Event 3 (\"apprehended\") might refer to the same individual, weaving a complex web of interconnected events. Capturing such patterns requires models capable of recognizing argument sharing and underlying semantic structures. However, traditional event argument extraction methods often treat each event as an isolated case, breaking the text into separate samples. This approach not only weakens the ability to detect event relationships but also makes it harder to capture deeper meanings, such as causal links and chronological order. Figure 1: Schematic diagram of event extraction (EE) and event argument extraction (EAE). Arguments are indicated with underlines. The EE model aims to extract all events simultaneously, while mainstream EAE models extract the arguments of one event through a single training session. In summary, numerous real-world documents depict multiple, interconnected events that span across sentences or even paragraphs. This document-level complexity presents two significant challenges for current event argument extraction. First, long-distance dependencies: the trigger words and arguments of events may be distributed in different sentences or even different paragraphs, making it difficult for traditional single-sentence modeling methods to capture these dispersed pieces of information. This long-distance dependency requires the model to possess cross-sentence reasoning and global modeling capabilities. Second, event co-occurrence: a document may contain multiple events, and there may be complex relationships between events, such as argument sharing, temporal order, or causal associations. Existing methods find it challenging to comprehensively capture the semantic dependencies in event co-occurrence, which affects the accuracy of extraction and overall performance. To handle the challenges previously outlined in this paper, we present the Semantic-Aware Prompt-based Argument Extractor (SPARE) model.