Paper: Active Learning by Labeling Features

ACL ID D09-1009
Title Active Learning by Labeling Features
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009

Methods that learn from prior informa- tion about input features such as general- ized expectation (GE) have been used to train accurate models with very little ef- fort. In this paper, we propose an ac- tive learning approach in which the ma- chine solicits “labels” on features rather than instances. In both simulated and real user experiments on two sequence label- ing tasks we show that our active learning method outperforms passive learning with features as well as traditional active learn- ing with instances. Preliminary experi- ments suggest that novel interfaces which intelligently solicit labels on multiple fea- tures facilitate more efficient annotation.