Paper: A Latent Variable Model for Generative Dependency Parsing

ACL ID W07-2218
Title A Latent Variable Model for Generative Dependency Parsing
Venue Conference on Parsing Technologies
Session Main Conference
Year 2007
Authors

We propose a generative dependency pars- ing model which uses binary latent variables to induce conditioning features. To define this model we use a recently proposed class of Bayesian Networks for structured predic- tion, Incremental Sigmoid Belief Networks. We demonstrate that the proposed model achieves state-of-the-art results on three dif- ferent languages. We also demonstrate that the features induced by the ISBN’s latent variables are crucial to this success, and show that the proposed model is particularly good on long dependencies.