Semisupervised learning has emerged as a popular framework for improving
modeling accuracy while controlling labeling cost. Based on an extension of
stochastic composite likelihood we quantify the asymptotic accuracy of
generative semi-supervised learning. In doing so, we complement
distribution-free analysis by providing an alternative framework to measure the
value associated with different labeling policies and resolve the fundamental
question of how much data to label and in what manner. We demonstrate our
approach with both simulation studies and real world experiments using naive
Bayes for text classification and MRFs and CRFs for structured prediction in
NLP.Comment: 12 pages, 9 figure
