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Over the last two and a half decades, computational linguistics has been revolutionized as a result of three closely related developments: increases in computing power, the advent of large linguistic datasets, and a paradigm shift toward probabilistic modeling. At the same time, similar theoretical developments in cognitive science have led to a view major aspects of human cognition as instances of rational statistical inference. These developments have set the stage for renewed interest in computational approaches to human language use. Correspondingly, this course covers some of the most exciting developments in computational psycholinguistics over the past decade. The course spans human language comprehension, production, and acquisition, and covers key phenomena from both phonetics and syntax. Students will learn key technical tools including probabilistic models, formal grammars, and decision theory, and how theory, computational modeling, and data can be combined to advance our fundamental understanding of human language use.
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This course is self-contained and there are no prerequisites, but students will benefit from having previous experience in any of the following areas: probability theory, syntax, formal grammars, psycholinguistics, statistics, and computational modeling.