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  1. 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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  • Mining a Crowdsourced Dictionary to Understand Consistency and Preference in Word Meanings.Brendan T. Johns - 2019 - Frontiers in Psychology 10.
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  • Shades of confusion: Lexical uncertainty modulates ad hoc coordination in an interactive communication task.Sonia K. Murthy, Thomas L. Griffiths & Robert D. Hawkins - 2022 - Cognition 225 (C):105152.
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  • 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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