Analogical inference from distributional structure: What recurrent neural networks can tell us about word learning

Analogical inference from distributional structure: What recurrent neural networks can tell us about word learning

One proposal that can explain the remarkable pace of word learning in young children is that they leverage the language-internal distributional similarity of familiar and novel words to make analogical inferences about possible meanings of novel words (Lany and Gómez, 2008; Lany and Saffran, 2011; S...

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Bibliographic Details
Main Authors: Philip A. Huebner, Jon A. Willits
Format: Article
Language:English
Published: Elsevier 2023-09-01
Series:Machine Learning with Applications
Subjects:
Distributional learning
Semantic development
Learning dynamics
Recurrent neural network
Online Access:http://www.sciencedirect.com/science/article/pii/S2666827023000312

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http://www.sciencedirect.com/science/article/pii/S2666827023000312

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