A Survey on Open Information Extraction from Rule-based Model to Large Language Model

• URL: http://arxiv.org/abs/2208.08690v7
• Date: Wed, 23 Oct 2024 19:36:50 GMT
• Title: A Survey on Open Information Extraction from Rule-based Model to Large Language Model
• Authors: Pai Liu, Wenyang Gao, Wenjie Dong, Lin Ai, Ziwei Gong, Songfang Huang, Zongsheng Li, Ehsan Hoque, Julia Hirschberg, Yue Zhang,
• Abstract summary: Open Information Extraction (OpenIE) represents a crucial NLP task aimed at deriving structured information from unstructured text. This survey paper provides an overview of OpenIE technologies spanning from 2007 to 2024, emphasizing a chronological perspective. The paper categorizes OpenIE approaches into rule-based, neural, and pre-trained large language models, discussing each within a chronological framework.
• Score: 29.017823043117144
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• Abstract: Open Information Extraction (OpenIE) represents a crucial NLP task aimed at deriving structured information from unstructured text, unrestricted by relation type or domain. This survey paper provides an overview of OpenIE technologies spanning from 2007 to 2024, emphasizing a chronological perspective absent in prior surveys. It examines the evolution of task settings in OpenIE to align with the advances in recent technologies. The paper categorizes OpenIE approaches into rule-based, neural, and pre-trained large language models, discussing each within a chronological framework. Additionally, it highlights prevalent datasets and evaluation metrics currently in use. Building on this extensive review, the paper outlines potential future directions in terms of datasets, information sources, output formats, methodologies, and evaluation metrics.

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