Active Learning for Natural Language Parsing and Information Extraction (1999)
In natural language acquisition, it is difficult to gather the annotated data needed for supervised learning; however, unannotated data is fairly plentiful. Active learning methods attempt to select for annotation and training only the most informative examples, and therefore are potentially very useful in natural language applications. However, existing results for active learning have only considered standard classification tasks. To reduce annotation effort while maintaining accuracy, we apply active learning to two non-classification tasks in natural language processing: semantic parsing and information extraction. We show that active learning can significantly reduce the number of annotated examples required to achieve a given level of performance for these complex tasks.
In Proceedings of the Sixteenth International Conference on Machine Learning (ICML-99), pp. 406-414, Bled, Slovenia, June 1999.

Mary Elaine Califf Ph.D. Alumni mecaliff [at] ilstu edu
Raymond J. Mooney Faculty mooney [at] cs utexas edu
Cynthia Thompson Ph.D. Alumni cindi [at] cs utah edu