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• W1574662932 abstract "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." @default.
• W1574662932 created "2016-06-24" @default.
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• W1574662932 date "2007-06-01" @default.
• W1574662932 modified "2023-09-24" @default.
• W1574662932 title "Structured Prediction Models via the Matrix-Tree Theorem" @default.
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