until converge the predictor space (corrupted with estimation error, or noise), then SVD-ASO can be interpreted as finding the "principle components" (or commonality) of these predictors (i.e., "what good predictors are like").
(2004) used word-cluster memberships induced from an unannotated corpus as features for named entity chunking.