Recent advances in machine learning have significantly impacted the field of information extraction, with Large Language Models (LLMs) playing a pivotal role in extracting structured information from unstructured text. This paper explores the challenges and limitations of current methodologies in structured entity extraction and introduces a novel approach to address these issues. We contribute to the field by first introducing and formalizing the task of Structured Entity Extraction (SEE), followed by proposing Approximate Entity Set OverlaP (AESOP) Metric designed to appropriately assess model performance on this task. Later, we propose a new model that harnesses the power of LLMs for enhanced effectiveness and efficiency through decomposing the entire extraction task into multiple stages. Quantitative evaluation and human side-by-side evaluation confirm that our model outperforms baselines, offering promising directions for future advancements in structured entity extraction.

翻译：近期机器学习的进展显著影响了信息提取领域，其中大型语言模型在从非结构化文本中提取结构化信息方面发挥着关键作用。本文探讨了当前结构化实体提取方法所面临的挑战与局限性，并提出了一种新方法以解决这些问题。我们通过首次引入并形式化结构化实体提取任务，随后提出用于适当评估模型在此任务上表现的近似实体集重叠度量（AESOP Metric），为该领域做出贡献。之后，我们提出了一种新模型，通过将整个提取任务分解为多个阶段来利用大型语言模型的强大能力，以提升效果与效率。定量评估与人工对比评估证实，我们的模型优于基线方法，为结构化实体提取的未来发展提供了有前景的方向。