Weakly Supervised Sequence Labeling
Weakly Supervised Sequence Labeling is a sequence labeling paradigm that operates with incomplete, noisy, or indirect supervision, differing from fully supervised approaches by leveraging limited or imprecise label information to train models effectively.
10
Publications
591
Citations
43
Authors
12
Institutions
Publications per year
2017–2026
5
Parents
10
SemEval-2021 Task 5: Toxic Spans Detection
John Pavlopoulos, Jeffrey Sorensen, Léo Laugier et al. · 2021 · 75 citations · Full text
1–5 of 10
43
2
h-index
2
h-index
2
h-index
2
h-index
2
h-index
1–5 of 43
12
Chan Zuckerberg Biohub San Francisco
San Francisco, United States
2 h-index
6 publications
354 citations
Chan Zuckerberg Initiative (United States)
Redwood City, United States
2 h-index
6 publications
354 citations
Stanford University
Stanford, United States
2 h-index
28 publications
1.7K citations

Athens University of Economics and Business
Athens, Greece
1 h-index
2 publications
150 citations
Palo Alto University
Palo Alto, United States
1 h-index
3 publications
288 citations
Austrian Research Institute for Artificial Intelligence
Vienna, Austria
1 h-index
1 publications
61 citations
University of Michigan
Ann Arbor, United States
1 h-index
1 publications
153 citations
Google (United States)
Mountain View, United States
1 h-index
1 publications
75 citations
1–8 of 12
Repository · 2 publications · 23 citations
2
h-index
1 publications · 23 citations
1
h-index
Repository · 1 publications · 22 citations
1
h-index
1 publications · 96 citations
1
h-index
Repository · 1 publications · 61 citations
1
h-index
1–5