7 found
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  1. Perceptual Inference Through Global Lexical Similarity.Brendan T. Johns & Michael N. Jones - 2012 - Topics in Cognitive Science 4 (1):103-120.
    The literature contains a disconnect between accounts of how humans learn lexical semantic representations for words. Theories generally propose that lexical semantics are learned either through perceptual experience or through exposure to regularities in language. We propose here a model to integrate these two information sources. Specifically, the model uses the global structure of memory to exploit the redundancy between language and perception in order to generate inferred perceptual representations for words with which the model has no perceptual experience. We (...)
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  2.  23
    Disentangling contextual diversity: Communicative need as a lexical organizer.Brendan T. Johns - 2021 - Psychological Review 128 (3):525-557.
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  3.  20
    A Large‐Scale Analysis of Variance in Written Language.Brendan T. Johns & Randall K. Jamieson - 2018 - Cognitive Science 42 (4):1360-1374.
    The collection of very large text sources has revolutionized the study of natural language, leading to the development of several models of language learning and distributional semantics that extract sophisticated semantic representations of words based on the statistical redundancies contained within natural language. The models treat knowledge as an interaction of processing mechanisms and the structure of language experience. But language experience is often treated agnostically. We report a distributional semantic analysis that shows written language in fiction books varies appreciably (...)
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  4.  10
    Mining a Crowdsourced Dictionary to Understand Consistency and Preference in Word Meanings.Brendan T. Johns - 2019 - Frontiers in Psychology 10.
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  5.  16
    The Role of Negative Information in Distributional Semantic Learning.Brendan T. Johns, Douglas J. K. Mewhort & Michael N. Jones - 2019 - Cognitive Science 43 (5):e12730.
    Distributional models of semantics learn word meanings from contextual co‐occurrence patterns across a large sample of natural language. Early models, such as LSA and HAL (Landauer & Dumais, 1997; Lund & Burgess, 1996), counted co‐occurrence events; later models, such as BEAGLE (Jones & Mewhort, 2007), replaced counting co‐occurrences with vector accumulation. All of these models learned from positive information only: Words that occur together within a context become related to each other. A recent class of distributional models, referred to as (...)
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  6.  23
    The Role of Semantic Diversity in Word Recognition across Aging and Bilingualism.Brendan T. Johns, Christine L. Sheppard, Michael N. Jones & Vanessa Taler - 2016 - Frontiers in Psychology 7.
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  7.  3
    Determining the Relativity of Word Meanings Through the Construction of Individualized Models of Semantic Memory.Brendan T. Johns - 2024 - Cognitive Science 48 (2):e13413.
    Distributional models of lexical semantics are capable of acquiring sophisticated representations of word meanings. The main theoretical insight provided by these models is that they demonstrate the systematic connection between the knowledge that people acquire and the experience that they have with the natural language environment. However, linguistic experience is inherently variable and differs radically across people due to demographic and cultural variables. Recently, distributional models have been used to examine how word meanings vary across languages and it was found (...)
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