  Exact parsing with finite state automata is deemed inappropriate 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.
