CEMA – Cost-Efficient Machine-Assisted Document Annotations

We study the problem of semantically annotating textual documents that are complex in the sense that the documents are long, feature rich, and domain specific. Due to their complexity, such annotation tasks require trained human workers, which are very expensive in both time and money. We propose CEMA, a method for deploying machine learning to assist humans in complex document annotation. CEMA estimates the human cost of annotating each document and selects the set of documents to be annotated that strike the best balance between model accuracy and human cost. We conduct experiments on complex annotation tasks in which we compare CEMA against other document selection and annotation strategies. Our results show that CEMA is the most cost-efficient solution for those tasks.

Authors

• Guowen Yuan University of Hong Kong
• Ben Kao University of Hong Kong
• Tien-Hsuan Wu University of Hong Kong

Keywords

• ML: Active Learning
• SNLP: Syntax -- Tagging, Chunking & Parsing