Topics in Cognitive Science 3 (2):303-345 (2011)
Abstract |
Abstract Since their inception, distributional models of semantics have been criticized as inadequate cognitive theories of human semantic learning and representation. A principal challenge is that the representations derived by distributional models are purely symbolic and are not grounded in perception and action; this challenge has led many to favor feature-based models of semantic representation. We argue that the amount of perceptual and other semantic information that can be learned from purely distributional statistics has been underappreciated. We compare the representations of three feature-based and nine distributional models using a semantic clustering task. Several distributional models demonstrated semantic clustering comparable with clustering-based on feature-based representations. Furthermore, when trained on child-directed speech, the same distributional models perform as well as sensorimotor-based feature representations of children’s lexical semantic knowledge. These results suggest that, to a large extent, information relevant for extracting semantic categories is redundantly coded in perceptual and linguistic experience. Detailed analyses of the semantic clusters of the feature-based and distributional models also reveal that the models make use of complementary cues to semantic organization from the two data streams. Rather than conceptualizing feature-based and distributional models as competing theories, we argue that future focus should be on understanding the cognitive mechanisms humans use to integrate the two sources
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Keywords | Child‐directed speech Latent Semantic Analysis Semantic categorization Word learning Co‐occurrence models Semantic modeling |
Categories | (categorize this paper) |
DOI | 10.1111/j.1756-8765.2010.01111.x |
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References found in this work BETA
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Citations of this work BETA
Three Symbol Ungrounding Problems: Abstract Concepts and the Future of Embodied Cognition.Guy Dove - 2016 - Psychonomic Bulletin and Review 4 (23):1109-1121.
Thinking in Words: Language as an Embodied Medium of Thought.Guy Dove - 2014 - Topics in Cognitive Science 6 (3):371-389.
Perceptual Inference Through Global Lexical Similarity.Brendan T. Johns & Michael N. Jones - 2012 - Topics in Cognitive Science 4 (1):103-120.
The Emotions of Abstract Words: A Distributional Semantic Analysis.Alessandro Lenci, Gianluca E. Lebani & Lucia C. Passaro - 2018 - Topics in Cognitive Science 10 (3):550-572.
Reconciling Embodied and Distributional Accounts of Meaning in Language.Mark Andrews, Stefan Frank & Gabriella Vigliocco - 2014 - Topics in Cognitive Science 6 (3):359-370.
View all 18 citations / Add more citations
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