Dependency Parsing with Dynamic Bayesian Network

Book One

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 20

Natural Language Processing and Speech Recognition

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.