We introduce a bootstrapping algorithm for regression that exploits word embedding models. We use it to infer four psycholinguistic properties of words: Familiarity, Age of Acquisition, Concreteness and Imagery and further populate the MRC Psycholinguistic Database with these properties. The approach achieves 0.88 correlation with human-produced values and the inferred psycholinguistic features lead to state-of-the-art results when used in a Lexical Simplification task.
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