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

Liu Pai, Wenyang Gao, Wenjie Dong, Lin Ai, Ziwei Gong, Songfang Huang, Li Zongsheng, Ehsan Hoque, Julia Hirschberg, Yue Zhang

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, this paper systematically reviews the evolution of task settings, data, evaluation metrics, and methodologies in the era of large language models, highlighting their mutual influence, comparing their capabilities, and examining their implications for open challenges and future research directions.
Anthology ID:
2024.findings-emnlp.560
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2024
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9586–9608
Language:
URL:
https://aclanthology.org/2024.findings-emnlp.560
DOI:
10.18653/v1/2024.findings-emnlp.560
Bibkey:
Cite (ACL):
Liu Pai, Wenyang Gao, Wenjie Dong, Lin Ai, Ziwei Gong, Songfang Huang, Li Zongsheng, Ehsan Hoque, Julia Hirschberg, and Yue Zhang. 2024. A Survey on Open Information Extraction from Rule-based Model to Large Language Model. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 9586–9608, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
A Survey on Open Information Extraction from Rule-based Model to Large Language Model (Pai et al., Findings 2024)
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PDF:
https://preview.aclanthology.org/dois-2013-emnlp/2024.findings-emnlp.560.pdf
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