Visual Analytic System for Subject Matter Expert Document Tagging using Information Retrieval and Semi-Supervised Machine Learning

摘要

We present a system that combines ambient visualization, information retrieval and machine learning to facilitate the ease and quality of document classification by subject matter experts for the purpose of organizing documents by “tags” inferred by the resultant classifiers. This system includes data collection, a language model, query exploration, feature selection, semi-supervised machine learning and a visual analytic workflow enabling non-data scientists to rapidly define, verify, and refine high-quality document classifiers.