The notions of concreteness and imageability, traditionally important in psycholinguistics, are gaining significance in semantic-oriented natural language processing tasks. In this paper we investigate the predictability of these two concepts via supervised learning, using word embeddings as explanatory variables. We perform predictions both within and across languages by exploiting collections of cross-lingual embeddings aligned to a single vector space. We show that the notions of concreteness and imageability are highly predictable both within and across languages, with a moderate loss of up to 20 in correlation when predicting across languages. We further show that the cross-lingual transfer via word embeddings is more efficient than the simple transfer via bilingual dictionaries.
trained an orthogonal transformation to reorder word embedding dimensions into one-dimensional ultradense subspaces, the output thereby being a lexicon. They trained the transformations for sentiment, concreteness and frequency. For obtaining training data for concreteness, they used the BWK database @cite_14 . They showed that concreteness and sentiment can be better extracted from embedding spaces than frequency, with a Kendall @math correlation coefficient of 0.623 for concreteness. further exploited this method to perform operations over the extracted dimensions, such as given a concrete word like , find the related, but abstract word .
@cite_14
1974991592
Concreteness ratings are presented for 37,058 English words and 2,896 two-word expressions (such as zebra crossing and zoom in), obtained from over 4,000 participants by means of a norming study using Internet crowdsourcing for data collection. Although the instructions stressed that the assessment of word concreteness would be based on experiences involving all senses and motor responses, a comparison with the existing concreteness norms indicates that participants, as before, largely focused on visual and haptic experiences. The reported data set is a subset of a comprehensive list of English lemmas and contains all lemmas known by at least 85 of the raters. It can be used in future research as a reference list of generally known English lemmas.