Modelling Human Syntax by Means of Probabilistic Dependency Grammars - 2007
Center for Language & Speech Processing(CLSP), JHU via YouTube
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Explore a comprehensive lecture on probabilistic dependency grammars and their role in computational linguistics. Delve into the limitations of context-free dependency grammars in accounting for linguistic phenomena such as non-projective word order, secondary dependencies, and punctuation. Examine a generative dependency model that addresses these challenges and learn about error-driven incremental parsing algorithms with repair. Discover how the dependency model assigns probabilities to partial dependency analyses and the need for incorporating time-dependence into the model for left-right incremental text processing. Gain insights from Matthias Buch-Kromann, head of the Computational Linguistics Group at Copenhagen Business School, as he shares his expertise in dependency treebanks, probabilistic dependency models, and computational models of human parsing and translation.
Syllabus
Modelling human syntax by means of probabilistic dependency grammars – Matthias Buch-Kromann - 2007
Taught by
Center for Language & Speech Processing(CLSP), JHU