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Title:Using known words to learn more words: A distributional model of child vocabulary acquisition
Author(s):Flores, Andrew Z
Advisor(s):Willits, Jon
Department / Program:Psychology
Discipline:Psychology
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(s):vocabulary
age of acquisition
distributional learning
prior knowledge
bootstrapping
Abstract:Why do children learn some words before others? A large body of behavioral research has identified properties of the language environment that facilitate word learning, emphasizing the importance of particularly informative language contexts. However, these findings have not informed research that uses distributional properties of words to predict vocabulary composition. In the current work, we introduce a predictor of word learning that emphasizes the role of prior knowledge. We investigate item-based variability in vocabulary development using lexical properties of distributional statistics derived from a large corpus of child-directed speech. Unlike previous analyses, we predicted word trajectories cross-sectionally, shedding light on trends in vocabulary development that may not have been evident at a single time point. We also show that regardless of a word’s grammatical class the best distributional predictor of whether a child knows a word is the number of other known words with which that word tends to co-occur.
Issue Date:2021-12-03
Type:Thesis
URI:http://hdl.handle.net/2142/114091
Rights Information:Copyright 2021 Andrew Flores
Date Available in IDEALS:2022-04-29
Date Deposited:2021-12


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