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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2024
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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 |
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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 |
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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. |
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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 |
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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 |
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A comprehensive survey on relation extraction: Recent advances and new frontiers |
title_sort |
comprehensive survey on relation extraction: recent advances and new frontiers |
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Institutional Knowledge at Singapore Management University |
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2024 |
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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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