Paper: An Analysis of Active Learning Strategies for Sequence Labeling Tasks

ACL ID D08-1112
Title An Analysis of Active Learning Strategies for Sequence Labeling Tasks
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2008
Authors

Active learning is well-suited to many prob- lems in natural language processing, where unlabeled data may be abundant but annota- tion is slow and expensive. This paper aims to shed light on the best active learning ap- proaches for sequence labeling tasks such as information extraction and document segmen- tation. Wesurveypreviouslyusedqueryselec- tion strategies for sequence models, and pro- pose several novel algorithms to address their shortcomings. We also conduct a large-scale empirical comparison using multiple corpora, which demonstrates that our proposed meth- ods advance the state of the art.