Duration: 1:28:58

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In this talk, I will discuss a few projects aimed at "closing the loop" for interactive natural language annotation and learning. In particular, I describe two systems that combine active and semi-supervised learning by asking humans to label both instance queries (e.g., passages of text) and feature queries (e.g., advice about words or capitalization patterns, and the class labels they imply). Empirical results from real user studies show that these systems are better than state-of-the-art "passive" learning and even instance-only "active" learning, in terms of accuracy given a fixed budget of annotation time. The results are quite replicable and also provide insight into human annotator behavior, suggesting how human factors can and should be taken into account for interactive machine learning.