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The notions of concreteness and imageability, traditionally important inpsycholinguistics, are gaining significance in semantic-oriented naturallanguage processing tasks. In this paper we investigate the predictability ofthese two concepts via supervised learning, using word embeddings asexplanatory variables. We perform predictions both within and across languagesby exploiting collections of cross-lingual embeddings aligned to a singlevector space. We show that the notions of concreteness and imageability arehighly predictable both within and across languages, with a moderate loss of upto 20% in correlation when predicting across languages. We further show thatthe cross-lingual transfer via word embeddings is more efficient than thesimple transfer via bilingual dictionaries.

Predicting Concreteness and Imageability of Words Within and Across Languages via Word Embeddings