Paper: An Experimental Comparison of Active Learning Strategies for Partially Labeled Sequences

ACL ID D14-1097
Title An Experimental Comparison of Active Learning Strategies for Partially Labeled Sequences
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

Active learning (AL) consists of asking human annotators to annotate automatically selected data that are assumed to bring the most bene- fit in the creation of a classifier. AL allows to learn accurate systems with much less anno- tated data than what is required by pure super- vised learning algorithms, hence limiting the tedious effort of annotating a large collection of data. We experimentally investigate the behav- ior of several AL strategies for sequence labeling tasks (in a partially-labeled sce- nario) tailored on Partially-Labeled Condi- tional Random Fields, on four sequence la- beling tasks: phrase chunking, part-of-speech tagging, named-entity recognition, and bio- entity recognition.