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

INFODENS: An Open-source Framework for Learning Text Representations