Structured prediction models via the matrix-tree theorem

Structured prediction models via the matrix-tree theorem (2007)

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by Terry Koo , Amir Globerson , Xavier Carreras , Michael Collins

Venue:	In EMNLP-CoNLL
Citations:	21 - 3 self

BibTeX

@INPROCEEDINGS{Koo07structuredprediction,
    author = {Terry Koo and Amir Globerson and Xavier Carreras and Michael Collins},
    title = {Structured prediction models via the matrix-tree theorem},
    booktitle = {In EMNLP-CoNLL},
    year = {2007}
}

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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. 1

Citations

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