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EMNLP
2007

Probabilistic Models of Nonprojective Dependency Trees

9 years 2 months ago
Probabilistic Models of Nonprojective Dependency Trees
A notable gap in research on statistical dependency parsing is a proper conditional probability distribution over nonprojective dependency trees for a given sentence. We exploit the Matrix Tree Theorem (Tutte, 1984) to derive an algorithm that efficiently sums the scores of all nonprojective trees in a sentence, permitting the definition of a conditional log-linear model over trees. While discriminative methods, such as those presented in McDonald et al. (2005b), obtain very high accuracy on standard dependency parsing tasks and can be trained and applied without marginalization, “summing trees” permits some alternative techniques of interest. Using the summing algorithm, we present competitive experimental results on four nonprojective languages, for maximum conditional likelihood estimation, minimum Bayes-risk parsing, and hidden variable training.
David A. Smith, Noah A. Smith
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where EMNLP
Authors David A. Smith, Noah A. Smith
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