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Learn about computational approaches to modeling human syntactic structures through probabilistic dependency grammars in this lecture by Matthias Buch-Kromann from Copenhagen Business School's Center for Computational Modelling of Language. Explore how dependency grammar frameworks can be enhanced with probabilistic methods to better capture the statistical patterns found in natural language syntax. Discover the theoretical foundations of dependency parsing and how probabilistic models can account for syntactic ambiguity and variation in human language processing. Examine practical applications of these computational models in natural language processing tasks and their implications for understanding how humans parse and generate syntactic structures. Gain insights into the intersection of computational linguistics, cognitive science, and statistical modeling as applied to syntactic analysis.
