When faced with the task of building accurate classifiers, active learning is often a beneficial tool for minimizing the requisite costs of human annotation. Traditional active learning schemes query a human for labels on intelligently chosen examples. However, human effort can also be expended in collecting alternative forms of annotation. For example, one may attempt to learn a text classifier by labeling words associated with a class, instead of, or in addition to, documents. Learning from two different kinds of supervision adds a challenging dimension to the problem of active learning. In this paper, we present a unified approach to such active dual supervision: determining which feature or example a classifier is most likely to benefit from having labeled. Empirical results confirm that appropriately querying for both example and feature labels significantly reduces overall human effort—beyond what is possible through traditional one-dimensional active learning.
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