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.
Dependency parsing with Dynamic Bayesian Networks was considered in REF , with limited success.
Dependency Parsing with Dynamic Bayesian Network
In proceedings of American Association for Artificial Intelligence AAAI 2005
Computer Science