关键词: 自然语言处理(NLP), 金融事件抽取, 标签重构, 大语言模型(LLM), 生成式事件抽取

Abstract: Abstract: Event extraction aims to automatically identify event triggers, event types, arguments, and argument roles from unstructured or semi-structured text and transform them into structured representations. Due to the complex terminology, cross-sentence information distribution, and implicit expressions in financial event texts, traditional classification-based event extraction methods heavily rely on manual annotations, exhibit limited capability in recognizing complex events, and encounter difficulties in joint training and optimization of pipeline subtasks. To address these issues, a Label Reconstruction-based Financial Event Extraction Model (LREE) built upon a Large Language Model (LLM) was proposed. First, financial event labels were reconstructed into event record sequences that better align with the generative objectives of large language models, thereby transforming the classification-based event extraction task into a generative task. Then, a Transformer-based large language model was adopted as the backbone, and fine-tuning was conducted using instruction tuning and Low-Rank Adaptation(LoRA) techniques. Finally, during the generative decoding process, joint identification of event types, triggers, arguments, and roles was synchronously achieved through sequence generation. Experimental results on ChFinAnn show that the LREE model built upon Qwen-3-4B achieves an F1 score of 89.9% on the test set. This represents an improvement of 60.0 percentage points over the original Qwen-3-4B without fine-tuning and 9.6 percentage points over the DEEM-PT(Document Level Financial Event Extraction Method Guided by Prompt Template). The findings verify the effectiveness of the label reconstruction strategy in improving structured event generation performance for financial texts.

Key words: Natural Language Processing(NLP), financial event extraction, label reconstruction, Large Language Models(LLMs), generative event extraction