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EMNLP
2010

Turbo Parsers: Dependency Parsing by Approximate Variational Inference

9 years 8 months ago
Turbo Parsers: Dependency Parsing by Approximate Variational Inference
We present a unified view of two state-of-theart non-projective dependency parsers, both approximate: the loopy belief propagation parser of Smith and Eisner (2008) and the relaxed linear program of Martins et al. (2009). By representing the model assumptions with a factor graph, we shed light on the optimization problems tackled in each method. We also propose a new aggressive online algorithm to learn the model parameters, which makes use of the underlying variational representation. The algorithm does not require a learning rate parameter and provides a single framework for a wide family of convex loss functions, including CRFs and structured SVMs. Experiments show state-of-the-art performance for 14 languages.
André F. T. Martins, Noah A. Smith, Eric P.
Added 11 Feb 2011
Updated 11 Feb 2011
Type Journal
Year 2010
Where EMNLP
Authors André F. T. Martins, Noah A. Smith, Eric P. Xing, Pedro M. Q. Aguiar, Mário A. T. Figueiredo
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