Structured Entity Extraction Using Large Language Models
CoRR(2024)
摘要
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.
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