[<cite>12]</cite> automatically estimate missing psycholinguistic properties in the MRC Database through a bootstrapping algorithm for regression. Their method exploits word embedding models and 15 lexical features, including the number of senses, synonyms, hyper-nyms and hyponyms for word in WordNet and also minimum, maximum and average distance between the word's senses in WordNet and the thesaurus' root sense. The Pearson correlation between the estimated score and the inferred score for familiarity was 0.846; 0.862 for AoA; 0.823 for imagenery and 0.869 for concretness, which is better than the results of [4] .