Painless labeling with application to text mining

Abstract

Labeled data is not readily available for many natural language domains, and it typically requires expensive human effort with considerable domain knowledge to produce a set of labeled data. In this paper, we propose a simple unsupervised system that helps us create a labeled resource for categorical data (e.g., a document set) us-ing only fifteen minutes of human input. We utilize the labeled resources to dis-cover important insights about the data. The entire process is domain independent, and demands no prior annotation samples, or rules specific to an annotation.