Active Learning for Constrained Dirichlet Process Mixture Models

Recent work applied Dirichlet Process Mixture Models to the task of verb clustering, incorporating supervision in the form of must-links and cannot-links constraints between instances. In this work, we introduce an active learning approach for constraint selection employing uncertainty-based sampling. We achieve substantial improvements over random selection on two datasets.

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