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Are distributional representations ready for the real world? Evaluating word vectors for grounded perceptual meaning

arXiv 1705.11168 ↗First posted 31 May 2017cs.CLcited by 19

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Abstract (arXiv)

Distributional word representation methods exploit word co-occurrences to build compact vector encodings of words. While these representations enjoy widespread use in modern natural language processing, it is unclear whether they accurately encode all necessary facets of conceptual meaning. In this paper, we evaluate how well these representations can predict perceptual and conceptual features of concrete concepts, drawing on two semantic norm datasets sourced from human participants. We find that several standard word representations fail to encode many salient perceptual features of concepts, and show that these deficits correlate with word-word similarity prediction errors. Our analyses provide motivation for grounded and embodied language learning approaches, which may help to remedy these deficits.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 May 2017.

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Citing papers per year: 2021: 7; 2022: 5; 2023: 1; 2024: 2; 2025: 2; 2026: 2.

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  12. 2021ALL Dolphins Are Intelligent and SOME Are Friendly: Probing BERT for Nouns' Semantic Properties and their Prototypicalitycited by 12
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  4. 2021Theoretical foundations and limits of word embeddings: what types of meaning can they capture?cited by 5
  5. 2025Seeing What Tastes Good: Revisiting Multimodal Distributional Semantics in the Billion Parameter Eracited by 3
  6. 2026Quantifying and Predicting Disagreement in Graded Human Ratingscited by 1
  7. 2026Where Animacy Lives in Large Language Models: Tracing the Circuits of the Animacy Conceptcited by 0