Using known words to learn more words: A distributional model of child vocabulary acquisition

Flores, Andrew Z

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https://hdl.handle.net/2142/114091 Copy

Description

Title

Using known words to learn more words: A distributional model of child vocabulary acquisition

Author(s)

Flores, Andrew Z

Issue Date

2021-12-03

Director of Research (if dissertation) or Advisor (if thesis)

Willits, Jon

Department of Study

Psychology

Discipline

Psychology

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(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.

Graduation Semester

2021-12

Type of Resource

Thesis

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http://hdl.handle.net/2142/114091

Copyright and License Information

Copyright 2021 Andrew Flores

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