Closing the Loop: Fast, Interactive Semi-Supervised Annotation With Queries on Features and Instances
Burr Settles · 2011
This paper describes DUALIST, an active learning annotation paradigm which solicits and learns from labels on both features (e.g., words) and instances (e.g., documents). We present a novel semi-supervised training algorithm developed for this setting, which is (1) fast enough to support real-time interactive speeds, and (2) at least as accurate as preexisting methods for learning with mixed feature and instance labels. Human annotators in user studies were able to produce near-stateof-the-art classifiers—on several corpora in a variety of application domains—with only a few minutes of effort. 1