One obvious direction is to use the whole Penn Treebank as labeled data and use some other unannotated data source as unlabeled data for semi-supervised training.

0 Step 2, based on the learned parameter weights from the labeled data , update 6 and Yj on each unlabeled sentence alternatively:

However, a key drawback of supervised training algorithms is their dependence on labeled data , which is usually very difficult to obtain.

This loss function has the advantage that the entire training objective on both the labeled and unlabeled data now becomes convex, since it consists of a convex structured large margin loss on labeled data and a convex least squares loss on unlabeled data.

In particular, we investigate a semi-supervised approach for structured large margin training, where the objective is a combination of two convex functions, the structured large margin loss on labeled data and the least squares loss on unlabeled data.

for structured large margin training, whose objective is a combination of two convex terms: the supervised structured large margin loss on labeled data and the cheap least squares loss on unlabeled data.

By combining the convex structured SVM loss on labeled data (shown in Equation (5)) and the convex least squares loss on unlabeled data (shown in Equation (8)), we obtain a semi-supervised structured large margin loss

Our model achieves high accuracy, without any explicitly labeled data except the user provided opinion ratings.

When labeled data exists, this problem can be solved effectively using a wide variety of methods available for text classification and information extraction (Manning and Schutze, 1999).

However, labeled data is often hard to come by, especially when one considers all possible domains of products and services.