Distributional Semantics and Linguistic Theory

Boleda, Gemma

doi:10.1146/annurev-linguistics-011619-030303

Cited by 171 publications

(117 citation statements)

References 67 publications

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“…Word meanings cannot be dissociated from word use. The meaning of "bedroom" derives in part from the existence of contrasting words ("bathroom", "kitchen") and the contexts in which these words are used [129]. What this means is that co-occurrence statistics between words can act as a kind of echo of real-world linkages and causal relationships.…”

Section: Box 3 Visual Knowledge From Language Statisticsmentioning

confidence: 99%

Effects of Language on Visual Perception

Lupyan

Rahman

Boroditsky

et al. 2020

Trends in Cognitive Sciences

138

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Does language change what we perceive? Does speaking different languages cause us to perceive things differently? We review the behavioral and electrophysiological evidence for the influence of language on perception, with an emphasis on the visual modality. Effects of language on perception can be observed both in higher-level processes such as recognition, and in lower-level processes such as discrimination and detection. A consistent finding is that language causes us to perceive in a more categorical way. Rather than being fringe or exotic, as they are sometimes portrayed, we discuss how effects of language on perception naturally arise from the interactive and predictive nature of perception.

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Section: Box 3 Visual Knowledge From Language Statisticsmentioning

confidence: 99%

Effects of Language on Visual Perception

Lupyan

Rahman

Boroditsky

et al. 2020

Trends in Cognitive Sciences

138

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“…Below, we provide a rough characterization of how different types of distributional semantic models build semantic knowledge. For a more complete picture of current and future directions, we refer the reader to Boleda (2020) and Lenci (2018), as well as Baroni et al (2014), Mandera et al (2017), and Wingfield and Connell (2019).…”

Section: Types Of Modelsmentioning

confidence: 99%

Building semantic memory from embodied and distributional language experience

Davis

Yee

2021

WIRES Cognitive Science

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Humans seamlessly make sense of a rapidly changing environment, using a seemingly limitless knowledgebase to recognize and adapt to most situations we encounter. This knowledgebase is called semantic memory. Embodied cognition theories suggest that we represent this knowledge through simulation: understanding the meaning of coffee entails re-instantiating the neural states involved in touching, smelling, seeing, and drinking coffee. Distributional semantic theories suggest that we are sensitive to statistical regularities in natural language, and that a cognitive mechanism picks up on these regularities and transforms them into usable semantic representations reflecting the contextual usage of language. These appear to present contrasting views on semantic memory, but do they? Recent years have seen a push toward combining these approaches under a common framework. These hybrid approaches augment our understanding of semantic memory in important ways, but current versions remain unsatisfactory in part because they treat sensory-perceptual and distributional-linguistic data as interacting but distinct types of data that must be combined. We synthesize several approaches which, taken together, suggest that linguistic and embodied experience should instead be considered as inseparably entangled: just as sensory and perceptual systems are reactivated to understand meaning, so are experience-based representations endemic to linguistic processing; further, sensory-perceptual experience is susceptible to the same distributional principles as language experience. This conclusion produces a characterization of semantic memory that accounts for the interdependencies between linguistic and embodied data 2 that arise across multiple timescales, giving rise to concept representations that reflect our shared and unique experiences.

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“…Advances in machine-learning, 41 combined with the availability of large corpora of digitized text, have now made it possible to estimate representations of word meanings -word embeddings -in a way that correlates with human semantic judgments with a surprising degree of subtlety. [42][43][44][45][46][47][48][49][50][51] Semantic representations derived from word embeddings capture both the range of contexts in which a word is used and the relative frequencies of those contexts. Comparing contexts of use across languages allows us to quantify in a data-driven way what is sometimes called "partial equivalence" 52 -similarities and differences in the semantic profiles of translation-equivalent pairs.…”

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confidence: 99%

Cultural influences on word meanings revealed through large-scale semantic alignment

2020

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If the structure of language vocabularies mirrors the structure of natural divisions that are universally perceived, then the meanings of words in different languages should closely align. In contrast, if shared word meanings are a product of shared culture, history, and geography, they may differ between languages in substantial but predictable ways. We analyzed the semantic neighbourhoods of 1,010 meanings in 41 languages. The most aligned words were from semantic domains with high internal structure (number, quantity, kinship). Words denoting natural kinds, common actions, and artifacts aligned much less well. Languages that are more geographically proximate, more historically related, and languages spoken by more similar cultures, had more aligned word meanings. These results provide evidence that the meanings of common words vary in ways that reflect the culture, history and geography of their users.

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Distributional Semantics and Linguistic Theory

Cited by 171 publications

References 67 publications

Effects of Language on Visual Perception

Effects of Language on Visual Perception

Building semantic memory from embodied and distributional language experience

Cultural influences on word meanings revealed through large-scale semantic alignment

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