A cost-reducing partial labeling estimator in text classification problem

Jiangning Chen, Zhibo Dai, Juntao Duan, Qianli Hu, Ruilin Li, Heinrich Matzinger, Ionel Popescu, Haoyan Zhai

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.

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