Joint Relation Extraction
Joint Relation Extraction is a method in information extraction that simultaneously identifies entities and their relational predicates within text, distinguishing itself from pipeline approaches by modeling entity recognition and relation classification as interdependent tasks within a unified framework.
194
Publications
15.6K
Citations
646
Authors
191
Institutions
Publications per year
2017–2026
157
194
Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme
Suncong Zheng, Feng Wang, Hongyun Bao et al. · 2017 · 749 citations · Full text
Multi-instance Multi-label Learning for Relation Extraction
Mihai Surdeanu, Julie Tibshirani, Ramesh Nallapati et al. · 2012 · 683 citations
Structured information extraction from scientific text with large language models
John Dagdelen, Alexander Dunn, Sang‐Hoon Lee et al. · Nature Communications · 2024 · 586 citations · Full text
Incremental Joint Extraction of Entity Mentions and Relations
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
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191

Chinese Academy of Sciences
Beijing, China
10 h-index
31 publications
9.8K citations
University of Illinois Urbana-Champaign
Urbana, United States
7 h-index
24 publications
2.7K citations

Beihang University
Beijing, China
6 h-index
17 publications
715 citations

National University of Defense Technology
Changsha, China
6 h-index
24 publications
910 citations
University of Chinese Academy of Sciences
Beijing, China
6 h-index
18 publications
4.6K citations
Stanford University
Stanford, United States
5 h-index
18 publications
2.7K citations
Tsinghua University
Beijing, China
5 h-index
9 publications
755 citations
Wuhan University
Wuhan, China
5 h-index
9 publications
662 citations
1–8 of 191
Repository · 20 publications · 646 citations
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Conference · 11 publications · 823 citations
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6 publications · 260 citations
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5 publications · 528 citations
6
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4 publications · 392 citations
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