A cost-reducing partial labeling estimator in text classification problem
This incremental method reduces labeling costs for applications like crowdsourcing and NLP.
The paper tackles text classification with partial labels by assigning negative-oriented labels to ambiguous examples, resulting in faster convergence under certain conditions.
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.