Two robust semi-supervised learning algorithms for natural language processing
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Labeled data is often short in supply in natural language processing, but semi-supervised learning of structured variables is a difficult task. Positive results often rely on extensive parameter-tuning. In this talk, I present two new semi-supervised learning algorithms that have shown to be very robust across applications and datasets with no or fixed parameters.
This talk is part of the NLIP Seminar Series series.
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