  The advent of representation learning methods enabled large performance gains
on various language tasks, alleviating the need for manual feature engineering.
While engineered representations are usually based on some linguistic
understanding and are therefore more interpretable, learned representations are
harder to interpret. Empirically studying the complementarity of both
approaches can provide more linguistic insights that would help reach a better
compromise between interpretability and performance. We present INFODENS, a
framework for studying learned and engineered representations of text in the
context of text classification tasks. It is designed to simplify the tasks of
feature engineering as well as provide the groundwork for extracting learned
features and combining both approaches. INFODENS is flexible, extensible, with
a short learning curve, and is easy to integrate with many of the available and
widely used natural language processing tools.
