An experimental comparison of active learning strategies for partially labeled sequences

Abstract

Active learning (AL) consists of asking human annotators to annotate automatically selected data that are assumed to bring the most benefit in the creation of a classifier. AL allows to learn accurate systems with much less annotated data than what is required by pure supervised learning algorithms, hence limiting the tedious effort of annotating a large collection of data. We experimentally investigate the behavior of several AL strategies for sequence labeling tasks (in a partially-labeled scenario) tailored on Partially-Labeled Conditional Random Fields, on four sequence labeling tasks: phrase chunking, part-of-speech tagging, named-entity recognition, and bioentity recognition.