Automatic Label Generation
Automatic Label Generation is a methodological framework within labeling systems that autonomously produces descriptive labels for data instances by leveraging algorithmic rules or learned patterns, distinguishing itself from manual or semi-supervised approaches through its complete reliance on automated processes.
134
Publications
6.1K
Citations
549
Authors
211
Institutions
Publications per year
2017–2026
104
Parents
134
Snorkel: rapid training data creation with weak supervision
Alexander Ratner, Stephen H. Bach, Henry R. Ehrenberg et al. · The VLDB Journal · 2019 · 353 citations · Full text
Automatic Generation of Social Tags for Music Recommendation
Douglas Eck, Paul Lamere, Thierry Bertin-Mahieux et al. · neural information processing systems · 2007 · 184 citations
Automatically Labeled Data Generation for Large Scale Event Extraction
Yubo Chen, Shulin Liu, Xiang Zhang et al. · 2017 · 172 citations · Full text
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211

Zhejiang University
Hangzhou, China
5 h-index
14 publications
899 citations
University of Illinois Urbana-Champaign
Urbana, United States
5 h-index
18 publications
380 citations

Beihang University
Beijing, China
3 h-index
6 publications
173 citations

Chinese Academy of Sciences
Beijing, China
3 h-index
12 publications
1.1K citations
University of Chinese Academy of Sciences
Beijing, China
3 h-index
9 publications
850 citations
Dalian University of Technology
Dalian, China
2 h-index
8 publications
390 citations
Microsoft (United States)
Redmond, United States
2 h-index
14 publications
740 citations

Northwestern Polytechnical University
Xi'an, China
2 h-index
3 publications
150 citations
1–8 of 211
Repository · 9 publications · 217 citations
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3 publications · 145 citations
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2 publications · 95 citations
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2 publications · 75 citations
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2 publications · 168 citations
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