  We propose a new approach to address the text classification problems when
learning with partial labels is beneficial. Instead of offering each training
sample a set of candidate labels, we assign negative-oriented labels to the
ambiguous training examples if they are unlikely fall into certain classes. We
construct our new maximum likelihood estimators with self-correction property,
and prove that under some conditions, our estimators converge faster. Also we
discuss the advantages of applying one of our estimator to a fully supervised
learning problem. The proposed method has potential applicability in many
areas, such as crowdsourcing, natural language processing and medical image
analysis.
