  Machine Learning and Inference methods have become ubiquitous in our attempt
to induce more abstract representations of natural language text, visual
scenes, and other messy, naturally occurring data, and support decisions that
depend on it. However, learning models for these tasks is difficult partly
because generating the necessary supervision signals for it is costly and does
not scale. This paper describes several learning paradigms that are designed to
alleviate the supervision bottleneck. It will illustrate their benefit in the
context of multiple problems, all pertaining to inducing various levels of
semantic representations from text.
