Relational Triple Extraction
Relational Triple Extraction is a natural language processing task that identifies and extracts semantically meaningful triples from unstructured text, where each triple consists of a subject entity, a relation, and an object entity, thereby structuring relational knowledge for downstream applications.
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2.2K
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114
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46
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2017–2026
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A Novel Cascade Binary Tagging Framework for Relational Triple Extraction
Zhepei Wei, Jianlin Su, Yue Wang et al. · 2020 · 622 citations · Full text
Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism
Xiangrong Zeng, Daojian Zeng, Shizhu He et al. · 2018 · 532 citations · Full text
PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction
Hengyi Zheng, Rui Wen, Xi Chen et al. · 2021 · 238 citations · Full text
Generating Coherent Event Schemas at Scale
Niranjan Balasubramanian, Stephen Soderland · 2013 · 111 citations · Full text
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Zhejiang University
Hangzhou, China
4 h-index
12 publications
569 citations

Northeastern University
Shenyang, China
4 h-index
19 publications
996 citations
Tencent (China)
Shenzhen, China
3 h-index
10 publications
1.7K citations
Rensselaer Polytechnic Institute
Troy, United States
2 h-index
5 publications
103 citations

University of Toyama
Toyama, Japan
2 h-index
2 publications
32 citations

Beijing Institute of Technology
Beijing, China
2 h-index
4 publications
76 citations
Tsinghua University
Beijing, China
2 h-index
3 publications
60 citations

Chinese Academy of Sciences
Beijing, China
2 h-index
2 publications
562 citations
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Repository · 3 publications · 75 citations
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1 publications · 57 citations
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1 publications · 13 citations
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Conference · 1 publications · 72 citations
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Conference · 1 publications · 68 citations
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