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Graph-Based Query Strategies for Active Learning

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2 Author(s)
Wei Wu ; Dept. of Electr. Eng., Univ. of Washington, Seattle, WA, USA ; Ostendorf, M.

This paper proposes two new graph-based query strategies for active learning in a framework that is convenient to combine with semi-supervised learning based on label propagation. The first strategy selects instances independently to maximize the change to a maximum entropy model using label propagation results in a gradient length measure of model change. The second strategy involves a batch criterion that integrates label uncertainty with diversity and density objectives. Experiments on sentiment classification demonstrate that both methods consistently improve over a standard active learning baseline, and that the batch criterion also gives consistent improvement over semi-supervised learning alone.

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Audio, Speech, and Language Processing, IEEE Transactions on  (Volume:21 ,  Issue: 2 )