Abney simply uses a uniform distribution over labels (e.g., parses), to reflect that the label is unknown.
abney defines a function K that is an upper bound on the negative log-likelihood, and shows his bootstrapping algorithms locally minimize K. We now present a generalization of abney's K function and relate it to another semi-supervised learning technique, entropy regularization (Brand, 1999;Grandvalet and Bengio, 2005;Jiao et al., 2006).