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Published 2017.
This book investigates the role that ambiguous evidence can play in the acquisition of syntax. To illustrate this, the book introduces a probabilistic learning model for syntactic parameters that learns a grammar of best ...
Published 2017.
This book investigates the role that ambiguous evidence can play in the acquisition of syntax. To illustrate this, the book introduces a probabilistic learning model for syntactic parameters that learns a grammar of best fit to the learner's evidence. The model is then applied to a range of cross-linguistic case studies - in Swiss German, Korean, and English - involving child errors, grammatical variability, and implicit negative evidence. Building on earlier work on language modeling, this book is unique for its focus on ambiguous evidence and its careful attention to the effects of parameters interacting with each other. This allows for a novel and principled account of several acquisition puzzles. With its inter-disciplinary approach, this book will be of broad interest to syntacticians, language acquisitionists, and cognitive scientists of language.
