Semi-supervised wrapper methods are concerned with building effective supervised classifiers from partially labeled data. Though previous works have succeeded in some fields, it is still difficult to apply semi-supervised wrapper methods to practice because the assumptions those methods rely on tend to be unrealistic in practice. For practical use, this paper proposes a novel semi-supervised wrapper method, Dual Teaching, whose assumptions are easy to set up. Dual Teaching adopts two external classifiers to estimate the false positives and false negatives of the base learner. Only if the recall of every external classifier is greater than zero and the sum of the precision is greater than one, Dual Teaching will train a base learner from partially labeled data as effectively as the fully-labeled-data-trained classifier. The effectiveness of Dual Teaching is proved in both theory and practice.
@cite_2 is characterized by the strategy that the learner uses its own predictions to teach itself. It starts by training a base learner from some labeled data and then evaluates the learner on the unlabeled data. Examples along with the labels predicted by the base learner are added to the training set and then the classifier is re-trained. Because the early mistakes made by the base learner would be reinforced by inputting the false labels to the early training set, self-training assumes that predictions of the base learner tend to be correct. However, it is unrealistic in some practical scenarios that a supervised model trained form a few labeled could classify amounts of unlabeled data successfully.
@cite_2
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We present a simple, but surprisingly effective, method of self-training a two-phase parser-reranker system using readily available unlabeled data. We show that this type of bootstrapping is possible for parsing when the bootstrapped parses are processed by a discriminative reranker. Our improved model achieves an f-score of 92.1 , an absolute 1.1 improvement (12 error reduction) over the previous best result for Wall Street Journal parsing. Finally, we provide some analysis to better understand the phenomenon.