Reading to Learn: Constructing Features from Semantic Abstracts

Machine learning offers a range of tools for training systems from data, but these methods are only as good as the underlying representation. This paper proposes to acquire representations for machine learning by reading text written to accommodate human learning. We propose a novel form of semantic analysis called reading to learn, where the goal is to obtain a high-level semantic abstract of multiple documents in a representation that facilitates learning. We obtain this abstract through a generative model that requires no labeled data, instead leveraging repetition across multiple documents. The semantic abstract is converted into a transformed feature space for learning, resulting in improved generalization on a relational learning task.