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Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data

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Figure

Table 1: The data sets are complex: they have dramatic class skews, highly inter-dependent fea-tures, and large amounts of data
Table 2: Label regularization outperforms other semi-supervised learning methods at 100 labeleddata points
Figure 1: BIOII: Label regularization (GE) outperforms all other methods. The x-axis representsincreasing numbers of labeled data instances
Figure 2: CoNLL03: Label regularization (GE) outperforms all other methods. The x-axis repre-sents increasing numbers of labeled instances per class, and the y-axis is accuracy.
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