"Dependency Parsing with Dynamic Bayesian Network"

Content areas: Bayesian Networks, NLP, Cognitive Modeling

authors: Virginia Savova, 
         Department of Cognitive Science 
         Johns Hopkins University
         3400 N. Charles St., Baltimore MD 21218
And
         Leonid Peshkin 
         Department of Systems Biology 
         Harvard Medical School
         200 Longwood Ave, Boston MA 02115

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.