Predicting Concreteness and Imageability of Words Within and Across Languages via Word Embeddings
This addresses a problem in psycholinguistics and semantic NLP by enabling efficient cross-lingual transfer, though it is incremental as it applies existing methods to new tasks.
The paper tackled predicting concreteness and imageability of words using supervised learning with word embeddings, showing high predictability within and across languages with up to a 20% correlation loss in cross-lingual predictions.
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.