Dependency parsing by belief propagation

  • Authors:
  • David A. Smith;Jason Eisner

  • Affiliations:
  • Johns Hopkins University, Balitmore, MD;Johns Hopkins University, Balitmore, MD

  • Venue:
  • EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
  • Year:
  • 2008

Quantified Score

Hi-index 0.00

Visualization

Abstract

We formulate dependency parsing as a graphical model with the novel ingredient of global constraints. We show how to apply loopy belief propagation (BP), a simple and effective tool for approximate learning and inference. As a parsing algorithm, BP is both asymptotically and empirically efficient. Even with second-order features or latent variables, which would make exact parsing considerably slower or NP-hard, BP needs only O(n3) time with a small constant factor. Furthermore, such features significantly improve parse accuracy over exact first-order methods. Incorporating additional features would increase the runtime additively rather than multiplicatively.