# A cost-reducing partial labeling estimator in text classification problem

## TL;DR

This paper introduces a novel partial labeling estimator for text classification that reduces labeling costs by assigning negative labels to ambiguous samples, with proven faster convergence and broad applicability.

## Contribution

The paper presents a new maximum likelihood estimator with self-correction for partial labels, improving convergence speed and applicability in various fields.

## Key findings

- Estimator converges faster under certain conditions
- Applicable to fully supervised learning with advantages
- Potential for cost reduction in labeling processes

## Abstract

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.