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In this paper, we propose a method for training neural networks when we have a large set of data with weak labels and a small amount of data with true labels.
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Aliaksei Severyn and Alessandro Moschitti · 2015
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Semeval-2016 task 4: Sentiment analysis in twitter
Preslav Nakov, Alan Ritter, Sara Rosenthal, Fabrizio Sebastiani, and Veselin Stoyanov · 2016
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Yuan Tang · 2016
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Daojian Zeng, Kang Liu, Yubo Chen, and Jun Zhao · 2015
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Swisscheese at semeval-2016 task 4: Sentiment classification using an ensemble of convolutional neural networks with distant supervision
Jan Deriu, Maurice Gonzenbach, Fatih Uzdilli, Aurelien Lucchi, Valeria De Luca, and Martin Jaggi · 2016
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Avoiding your teacher’s mistakes: Training neural networks with controlled weak supervision
Mostafa Dehghani, Aliaksei Severyn, Sascha Rothe, and Jaap Kamps
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Neural ranking models with weak supervision
Mostafa Dehghani, Hamed Zamani, Aliaksei Severyn, Jaap Kamps, and W. Bruce Croft
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Twitter sentiment analysis with deep convolutional neural networks
Aliaksei Severyn and Alessandro Moschitti
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Leveraging large amounts of weakly supervised data for multi-language sentiment classification
Jan Deriu, Aurelien Lucchi, Valeria De Luca, Aliaksei Severyn, Simon Müller, Mark Cieliebak, Thomas Hofmann, and Martin Jaggi · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
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Learning from noisy large-scale datasets with minimal supervision
Andreas Veit, Neil Alldrin, Gal Chechik, Ivan Krasin, Abhinav Gupta, and Serge Belongie · 2017
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