Exploring semantic changes across early childhood with dynamic word embeddings

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Abstract/Contents

Abstract: Child-directed speech (CDS), commonly known as baby talk, is a major source of linguistic input during early childhood. Caregivers such as parents adapt their language usage for children to facilitate their daily communication. Furthermore, CDS plays a crucial role in shaping their knowledge about the world. To study this input, researchers have begun applying distributional semantic models to large child-directed speech, extracting various patterns of word use/co-occurrence. In this work, we introduce NLP methods that were originally developed to study historical language change to analyze how caregivers change their language use when talking to younger and older children. Some words' usage changed more than others'; this variability could be predicted based on the word's properties at both the individual and category level. We primarily focus on English data in our study. These findings suggest that the patterns of word use may be tuned to children's developmental context, perhaps scaffolding the acquisition of new concepts and skills.

Type of resource	text
Date created	August 26, 2020

Author	Jiang, Hang
Degree granting institution	Stanford University, Symbolic Systems Program
Primary advisor	Frank, Michael C.
Advisor	Fourtassi, Abdellah

Subject	Conceptual learning
Subject	word embeddings
Subject	natural language processing
Subject	semantic change
Subject	child-directed speech
Subject	language and cognition
Genre	Thesis

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License: This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).

Preferred Citation: Jiang, Hang and Frank, Michael C. and Fourtassi, Abdellah. (2020). Exploring semantic changes across early childhood with dynamic word embeddings. Stanford Digital Repository. Available at: https://purl.stanford.edu/gx478zn9814

Master's Theses, Symbolic Systems Program, Stanford University

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