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EMNLP
2007

Structured Prediction Models via the Matrix-Tree Theorem

9 years 3 months ago
Structured Prediction Models via the Matrix-Tree Theorem
This paper provides an algorithmic framework for learning statistical models involving directed spanning trees, or equivalently non-projective dependency structures. We show how partition functions and marginals for directed spanning trees can be computed by an adaptation of Kirchhoff’s Matrix-Tree Theorem. To demonstrate an application of the method, we perform experiments which use the algorithm in training both log-linear and max-margin dependency parsers. The new training methods give improvements in accuracy over perceptron-trained models.
Terry Koo, Amir Globerson, Xavier Carreras, Michae
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where EMNLP
Authors Terry Koo, Amir Globerson, Xavier Carreras, Michael Collins
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