Tasker Hull


2020

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comp-syn: Perceptually Grounded Word Embeddings with Color
Bhargav Srinivasa Desikan | Tasker Hull | Ethan Nadler | Douglas Guilbeault | Aabir Abubakar Kar | Mark Chu | Donald Ruggiero Lo Sardo
Proceedings of the 28th International Conference on Computational Linguistics

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 forover 40,000 English words, each associated with crowd-sourced word concreteness judgments.