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Large labeled training sets are the critical building blocks of supervised learning methods and are key enablers of deep learning techniques.
Maximum likelihood estimation of observer error-rates using the em algorithm
A. P. Dawid and A. M. Skene · 1979
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Learning with an unreliable teacher
G. Lugosi · 1992
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Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
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Combining labeled and unlabeled data with co-training
A. Blum and T. Mitchell · 1998
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Constructing biological knowledge bases by extracting information from text sources
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Multi-relational learning, text mining, and semi-supervised learning for functional genomics
M.-A. Krogel and T. Scheffer · 2004
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Learning to extract relations from the web using minimal supervision
R. Bunescu and R. Mooney · 2007
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Distant supervision for relation extraction without labeled data
M. Mintz, S. Bills, R. Snow, and D. Jurafsky · 2009
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Modeling relations and their mentions without labeled text
S. Riedel, L. Yao, and A. McCallum · 2010
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Harnessing the crowdsourcing power of social media for disaster relief
H. Gao, G. Barbier, R. Goolsby, and D. Zeng · 2011
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Iterative learning for reliable crowdsourcing systems
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Label-noise robust logistic regression and its applications
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An improved corpus of disease mentions in pubmed citations
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Boosting: Foundations and algorithms
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Aggregating crowdsourced binary ratings
N. Dalvi, A. Dasgupta, R. Kumar, and V. Rastogi · 2013
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Combining generative and discriminative model scores for distant supervision
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Overview of the english slot filling track at the tac2014 knowledge base population evaluation
M. Surdeanu and H. Ji · 2014
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Spectral methods meet em: A provably optimal algorithm for crowdsourcing
Y. Zhang, X. Chen, D. Zhou, and M. I. Jordan · 2014
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Scalable semi-supervised aggregation of classifiers
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Comprehensive and reliable crowd assessment algorithms
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Large-scale extraction of gene interactions from full-text literature using deepdive
E. K. Mallory, C. Zhang, C. Ré, and R. B. Altman · 2015
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Incremental knowledge base construction using deepdive
J. Shin, S. Wu, F. Wang, C. De Sa, C. Zhang, and C. Ré · 2015
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Stanford’s 2014 slot filling systems
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Consistency of weighted majority votes
D. Berend and A. Kontorovich · 2014
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Ranking and combining multiple predictors without labeled data
F. Parisi, F. Strino, B. Nadler, and Y. Kluger · 2014
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Pattern learning for relation extraction with a hierarchical topic model
E. Alfonseca, K. Filippova, J.-Y. Delort, and G. Garrido
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Knowledge-based weak supervision for information extraction of overlapping relations
R. Hoffmann, C. Zhang, X. Ling, L. Zettlemoyer, and D. S. Weld
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Learning with noisy labels
N. Natarajan, I. S. Dhillon, P. K. Ravikumar, and A. Tewari
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Multilingual relation extraction using compositional universal schema
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Data programming with ddlite: putting humans in a different part of the loop
H. R. Ehrenberg, J. Shin, A. J. Ratner, J. A. Fries, and C. Ré · 2016
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Visual genome: Connecting language and vision using crowdsourced dense image annotations
R. Krishna, Y. Zhu, O. Groth, J. Johnson, K. Hata, J. Kravitz, S. Chen, Y. Kalantidis, L.-J. Li, D. A. Shamma, et al · 2016
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