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 over the course of development. In this work, we leverage NLP methods that were originally developed to study historical language change to compare caregivers' use of words 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. 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. Furthermore, we extend this framework to analyze multiple languages.
Speaker: Hang Jiang, Stanford
Contact:Website: Click to Visit
Save this Event:iCalendar
Windows Live Calendar