Dependency Parsing with Dynamic Bayesian Network

Virginia Savova, Leonid Peshkin

Exact parsing with finite state automata is deemed inapropriate because of the unbounded non-locality languages overwhelmingly exhibit. We propose a way to structure the parsing task in order to make it amenable to local classification methods. This allows us to build a Dynamic Bayesian Network which uncovers the syntactic dependency structure of English sentences. Experiments with the Wall Street Journal demonstrate that the model successfully learns from labeled data.

Content Area: 14. Natural Language Processing & Speech Recognition

Subjects: 13. Natural Language Processing; 4. Cognitive Modeling

Submitted: May 10, 2005


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