A comprehensive survey on relation extraction: Recent advances and new frontiers

Relation extraction (RE) involves identifying the relations between entities from underlying content. RE serves as the foundation for many natural language processing (NLP) and information retrieval applications, such as knowledge graph completion and question answering. In recent years, deep neural...

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Main Authors: ZHAO, Xiaoyan, DENG, Yang, YANG, Min, WANG, Lingzhi, ZHANG, Rui, CHENG, Hong, LAM, Wai, SHEN, Ying, XU, Ruifeng
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Language:English
Published: Institutional Knowledge at Singapore Management University 2024
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Online Access:https://ink.library.smu.edu.sg/sis_research/9098
https://ink.library.smu.edu.sg/context/sis_research/article/10101/viewcontent/3674501.pdf
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spelling sg-smu-ink.sis_research-101012024-10-18T07:13:10Z A comprehensive survey on relation extraction: Recent advances and new frontiers ZHAO, Xiaoyan DENG, Yang YANG, Min WANG, Lingzhi ZHANG, Rui CHENG, Hong LAM, Wai SHEN, Ying XU, Ruifeng Relation extraction (RE) involves identifying the relations between entities from underlying content. RE serves as the foundation for many natural language processing (NLP) and information retrieval applications, such as knowledge graph completion and question answering. In recent years, deep neural networks have dominated the field of RE and made noticeable progress. Subsequently, the large pre-trained language models (PLMs) have taken the state-of-the-art RE to a new level. This survey provides a comprehensive review of existing deep learning techniques for RE. First, we introduce RE resources, including datasets and evaluation metrics. Second, we propose a new taxonomy to categorize existing works from three perspectives, i.e., text representation, context encoding, and triplet prediction. Third, we discuss several important challenges faced by RE and summarize potential techniques to tackle these challenges. Finally, we outline some promising future directions and prospects in this field. This survey is expected to facilitate researchers’ collaborative efforts to address the challenges of real-world RE systems. 2024-11-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/9098 info:doi/10.1145/3674501 https://ink.library.smu.edu.sg/context/sis_research/article/10101/viewcontent/3674501.pdf http://creativecommons.org/licenses/by/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Computing methodologies Natural language processing Neural networks Databases and Information Systems OS and Networks
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Computing methodologies
Natural language processing
Neural networks
Databases and Information Systems
OS and Networks
spellingShingle Computing methodologies
Natural language processing
Neural networks
Databases and Information Systems
OS and Networks
ZHAO, Xiaoyan
DENG, Yang
YANG, Min
WANG, Lingzhi
ZHANG, Rui
CHENG, Hong
LAM, Wai
SHEN, Ying
XU, Ruifeng
A comprehensive survey on relation extraction: Recent advances and new frontiers
description Relation extraction (RE) involves identifying the relations between entities from underlying content. RE serves as the foundation for many natural language processing (NLP) and information retrieval applications, such as knowledge graph completion and question answering. In recent years, deep neural networks have dominated the field of RE and made noticeable progress. Subsequently, the large pre-trained language models (PLMs) have taken the state-of-the-art RE to a new level. This survey provides a comprehensive review of existing deep learning techniques for RE. First, we introduce RE resources, including datasets and evaluation metrics. Second, we propose a new taxonomy to categorize existing works from three perspectives, i.e., text representation, context encoding, and triplet prediction. Third, we discuss several important challenges faced by RE and summarize potential techniques to tackle these challenges. Finally, we outline some promising future directions and prospects in this field. This survey is expected to facilitate researchers’ collaborative efforts to address the challenges of real-world RE systems.
format text
author ZHAO, Xiaoyan
DENG, Yang
YANG, Min
WANG, Lingzhi
ZHANG, Rui
CHENG, Hong
LAM, Wai
SHEN, Ying
XU, Ruifeng
author_facet ZHAO, Xiaoyan
DENG, Yang
YANG, Min
WANG, Lingzhi
ZHANG, Rui
CHENG, Hong
LAM, Wai
SHEN, Ying
XU, Ruifeng
author_sort ZHAO, Xiaoyan
title A comprehensive survey on relation extraction: Recent advances and new frontiers
title_short A comprehensive survey on relation extraction: Recent advances and new frontiers
title_full A comprehensive survey on relation extraction: Recent advances and new frontiers
title_fullStr A comprehensive survey on relation extraction: Recent advances and new frontiers
title_full_unstemmed A comprehensive survey on relation extraction: Recent advances and new frontiers
title_sort comprehensive survey on relation extraction: recent advances and new frontiers
publisher Institutional Knowledge at Singapore Management University
publishDate 2024
url https://ink.library.smu.edu.sg/sis_research/9098
https://ink.library.smu.edu.sg/context/sis_research/article/10101/viewcontent/3674501.pdf
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