  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.
