Dependency Parsing with Dynamic Bayesian Network

Virginia Savova and Leonid Peshkin

Johns Hopkins University and Harvard University

Abstract:

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.





Leonid Peshkin 2005-09-22