  Hand-engineered feature sets are a well understood method for creating robust
NLP models, but they require a lot of expertise and effort to create. In this
work we describe how to automatically generate rich feature sets from simple
units called featlets, requiring less engineering. Using information gain to
guide the generation process, we train models which rival the state of the art
on two standard Semantic Role Labeling datasets with almost no task or
linguistic insight.
