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

arXiv: https://arxiv.org/abs/1807.02903 · HTML: https://mlsanity.com/paper/1807.02903/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2018.

## Built on by (most cited first, 11 of 11)

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- [Augmenting semantic lexicons using word embeddings and transfer learning](https://mlsanity.com/paper/2109.09010/) (2021)
- [Verifying Claims About Metaphors with Large-Scale Automatic Metaphor Identification](https://mlsanity.com/paper/2404.01029/) (2024)
- [NU HLT at CMCL 2022 Shared Task: Multilingual and Crosslingual Prediction of Human Reading Behavior in Universal Language Space](https://mlsanity.com/paper/2202.10855/) (2022)
- [Automatic generation of a large dictionary with concreteness/abstractness ratings based on a small human dictionary](https://mlsanity.com/paper/2206.06200/) (2022)
- [Bridging Natural Language Processing and Psycholinguistics: computationally grounded semantic similarity datasets for Basque and Spanish](https://mlsanity.com/paper/2304.09616/) (2023)
- [MetaClimage: A novel database of visual metaphors related to Climate Change, with costs and benefits analysis](https://mlsanity.com/paper/2507.09225/) (2025)
- [CREATE: Creativity Evaluation of VLMs using the Visual Remote Associates Test](https://mlsanity.com/paper/2610.18569/) (2026)

## Abstract (arXiv)

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.