Paper: Spectral Dependency Parsing with Latent Variables

ACL ID D12-1019
Title Spectral Dependency Parsing with Latent Variables
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2012

Recently there has been substantial interest in using spectral methods to learn generative se- quence models like HMMs. Spectral meth- ods are attractive as they provide globally con- sistent estimates of the model parameters and are very fast and scalable, unlike EM meth- ods, which can get stuck in local minima. In this paper, we present a novel extension of this class of spectral methods to learn depen- dency tree structures. We propose a simple yet powerful latent variable generative model for dependency parsing, and a spectral learn- ing method to efficiently estimate it. As a pi- lot experimental evaluation, we use the spec- tral tree probabilities estimated by our model to re-rank the outputs of a near state-of-the- art parser. Our approach gives us a moderate reduction in error of ...