Appears in Proceedings of the Sixteenth International Machine Learning Conference,

Abstract

In natural language acquisition, it is di#- cult 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