Dependency Parsing with Dynamic Bayesian Network

Virginia Savova; Leonid Peshkin

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Dependency Parsing with Dynamic Bayesian Network

Virginia Savova·Leonid Peshkin

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Abstract

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.

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Virginia Savova, Leonid Peshkin. 2007-03-27. Dependency Parsing with Dynamic Bayesian Network. https://arxiv.org/abs/cs/0703135

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