A Survey on Open Information Extraction from Rule-based Model to Large Language Model
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 considers how traditional OpenIE research can inspire future IE research in the LLM era, aiming to provide insights into the past, present, and future of OpenIE methodologies and applications.
Paper Type: Long
Research Area: Information Extraction
Research Area Keywords: open information extraction, multilingual extraction
Contribution Types: Surveys
Languages Studied: English, Chinese
Submission Number: 3553
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