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  1. Finding Structure in One Child's Linguistic Experience.Wentao Wang, Wai Keen Vong, Najoung Kim & Brenden M. Lake - 2023 - Cognitive Science 47 (6):e13305.
    Neural network models have recently made striking progress in natural language processing, but they are typically trained on orders of magnitude more language input than children receive. What can these neural networks, which are primarily distributional learners, learn from a naturalistic subset of a single child's experience? We examine this question using a recent longitudinal dataset collected from a single child, consisting of egocentric visual data paired with text transcripts. We train both language-only and vision-and-language neural networks and analyze the (...)
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  • Exploring Patterns of Stability and Change in Caregivers' Word Usage Across Early Childhood.Hang Jiang, Michael C. Frank, Vivek Kulkarni & Abdellah Fourtassi - 2022 - Cognitive Science 46 (7):e13177.
    The linguistic input children receive across early childhood plays a crucial role in shaping their knowledge about the world. To study this input, researchers have begun applying distributional semantic models to large corpora of child‐directed speech, extracting various patterns of word use/co‐occurrence. Previous work using these models has not measured how these patterns may change throughout development, however. In this work, we leverage natural language processing methods—originally developed to study historical language change—to compare caregivers' use of words when talking to (...)
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