Supervised Relation Extraction
Supervised Relation Extraction is a relation extraction paradigm that operates under the assumption of having access to manually annotated training data, where the relations between entities are explicitly labeled, enabling the induction of a predictive model through supervised learning techniques.
84
Publications
9.8K
Citations
299
Authors
109
Institutions
Publications per year
2017–2026
54
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84
Distant supervision for relation extraction without labeled data
Mike D. Mintz, Steven Bills, Rion Snow et al. · 2009 · 2.9K citations · Full text
Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
Daojian Zeng, Kang Liu, Yubo Chen et al. · 2015 · 1.2K citations · Full text
Position-aware Attention and Supervised Data Improve Slot Filling
Yuhao Zhang, Victor W. Zhong, Danqi Chen et al. · 2017 · 839 citations · Full text
A Soft-label Method for Noise-tolerant Distantly Supervised Relation Extraction
Tianyu Liu, Kexiang Wang, Baobao Chang et al. · 2017 · 164 citations · Full text
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109
Stanford University
Stanford, United States
4 h-index
18 publications
16.6K citations
New York University
New York, United States
4 h-index
9 publications
763 citations
University of Illinois Urbana-Champaign
Urbana, United States
4 h-index
10 publications
382 citations

Beijing Institute of Technology
Beijing, China
4 h-index
15 publications
446 citations
University of Washington
Seattle, United States
4 h-index
6 publications
510 citations
Google (United States)
Mountain View, United States
3 h-index
8 publications
746 citations
Jilin University
Changchun, China
3 h-index
11 publications
223 citations

University of Electronic Science and Technology of China
Chengdu, China
3 h-index
5 publications
100 citations
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4 publications · 104 citations
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Repository · 4 publications · 198 citations
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Conference · 3 publications · 90 citations
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3 publications · 58 citations
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2 publications · 181 citations
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