Painless Labeling with Application to Text Mining

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) using only fifteen minutes of human input. We utilize the labeled resources to discover important insights about the data. The entire process is domain independent, and demands no prior annotation samples, or rules specific to an annotation.

• Type: Journal
• Year: 2015
• Where: ACL
• Authors: Sajib Dasgupta
• Keywords: ACL 2015, Computational Linguistics