# comp-syn: Perceptually Grounded Word Embeddings with Color

arXiv: https://arxiv.org/abs/2010.04292 · HTML: https://mlsanity.com/paper/2010.04292/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [Shades of confusion: Lexical uncertainty modulates ad hoc coordination in an interactive communication task](https://mlsanity.com/paper/2105.06546/) (2021)
- [Divergences in Color Perception between Deep Neural Networks and Humans](https://mlsanity.com/paper/2309.05809/) (2023)
- [Signal in Noise: Exploring Meaning Encoded in Random Character Sequences with Character-Aware Language Models](https://mlsanity.com/paper/2203.07911/) (2022)

## Abstract (arXiv)

Popular approaches to natural language processing create word embeddings based on textual co-occurrence patterns, but often ignore embodied, sensory aspects of language. Here, we introduce the Python package comp-syn, which provides grounded word embeddings based on the perceptually uniform color distributions of Google Image search results. We demonstrate that comp-syn significantly enriches models of distributional semantics. In particular, we show that (1) comp-syn predicts human judgments of word concreteness with greater accuracy and in a more interpretable fashion than word2vec using low-dimensional word-color embeddings, and (2) comp-syn performs comparably to word2vec on a metaphorical vs. literal word-pair classification task. comp-syn is open-source on PyPi and is compatible with mainstream machine-learning Python packages. Our package release includes word-color embeddings for over 40,000 English words, each associated with crowd-sourced word concreteness judgments.