emnlp emnlp2010 emnlp2010-110 emnlp2010-110-reference knowledge-graph by maker-knowledge-mining

110 emnlp-2010-Turbo Parsers: Dependency Parsing by Approximate Variational Inference


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Author: Andre Martins ; Noah Smith ; Eric Xing ; Pedro Aguiar ; Mario Figueiredo

Abstract: 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, includ- ing CRFs and structured SVMs. Experiments show state-of-the-art performance for 14 languages.


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