  Many real-world applications require automated data annotation, such as
identifying tissue origins based on gene expressions and classifying images
into semantic categories. Annotation classes are often numerous and subject to
changes over time, and annotating examples has become the major bottleneck for
supervised learning methods. In science and other high-value domains, large
repositories of data samples are often available, together with two sources of
organic supervision: a lexicon for the annotation classes, and text
descriptions that accompany some data samples. Distant supervision has emerged
as a promising paradigm for exploiting such indirect supervision by
automatically annotating examples where the text description contains a class
mention in the lexicon. However, due to linguistic variations and ambiguities,
such training data is inherently noisy, which limits the accuracy of this
approach. In this paper, we introduce an auxiliary natural language processing
system for the text modality, and incorporate co-training to reduce noise and
augment signal in distant supervision. Without using any manually labeled data,
our EZLearn system learned to accurately annotate data samples in functional
genomics and scientific figure comprehension, substantially outperforming
state-of-the-art supervised methods trained on tens of thousands of annotated
examples.
