Paper: Stopping Criteria for Active Learning of Named Entity Recognition

ACL ID C08-1059
Title Stopping Criteria for Active Learning of Named Entity Recognition
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2008
Authors

Active learning is a proven method for re- ducing the cost of creating the training sets thatarenecessaryforstatisticalNLP.How- ever, there has been little work on stopping criteria for active learning. An operational stopping criterion is necessary to be able to use active learning in NLP applications. We investigate three different stopping cri- teria for active learning of named entity recognition (NER) and show that one of them, gradient-based stopping, (i) reliably stops active learning, (ii) achieves near- optimal NER performance, (iii) and needs only about 20% as much training data as exhaustive labeling.