Difference between revisions of "Koo and Collins ACL 2010"
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Dependency parsing is defined as a search for the highest-scoring analysis of a sentence <math>\, x</math>: | Dependency parsing is defined as a search for the highest-scoring analysis of a sentence <math>\, x</math>: | ||
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<math>y^*(x) = _{y\in Y(x)}^{argmax}\textrm{Score}(x,y)</math> | <math>y^*(x) = _{y\in Y(x)}^{argmax}\textrm{Score}(x,y)</math> | ||
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where <math>\, Y(x) </math> is the set of all trees compatible with <math>\, x </math> and <math>\, \textrm{Score}(x,y) </math> evaluates the event that tree <math>\, y </math> is the analysis of <math>\, x </math>. Directly solving the equation is unfeasible because the number of possible trees grow exponentially with the length of the sentence. A common strategy is to factor each dependency tree into small parts which can be scored individually, then: | where <math>\, Y(x) </math> is the set of all trees compatible with <math>\, x </math> and <math>\, \textrm{Score}(x,y) </math> evaluates the event that tree <math>\, y </math> is the analysis of <math>\, x </math>. Directly solving the equation is unfeasible because the number of possible trees grow exponentially with the length of the sentence. A common strategy is to factor each dependency tree into small parts which can be scored individually, then: | ||
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<math>\textrm{Score}(x,y) = \sum_{p \in y}\textrm{ScorePart}(x,p)</math> | <math>\textrm{Score}(x,y) = \sum_{p \in y}\textrm{ScorePart}(x,p)</math> | ||
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The ''order'' of a part is defined according to the number of dependencies it contains, a terminology used in previous parsing algorithms, such as first-order factorization and second-order sibling factorization. The factorizations in this new third-order parser uses the following parts: | The ''order'' of a part is defined according to the number of dependencies it contains, a terminology used in previous parsing algorithms, such as first-order factorization and second-order sibling factorization. The factorizations in this new third-order parser uses the following parts: | ||
Revision as of 21:35, 25 November 2011
Contents
Citation
Koo, T. and Collins, M. 2010. Efficient Third-Order Dependency Parsers. In Proceedings of ACL, pp. 1-11. Association for Computational Linguistics.
Online version
Summary
This paper presents a higher-order dependency parser that can evaluate substructures containing three dependencies, using both sibling-style and grandchild-style interactions. The algorithms presented require only time and were evaluated on the Penn Treebank and the Prague Dependency Treebank. The implementation code was publicly released [1].
Background
Dependency parsing is defined as a search for the highest-scoring analysis of a sentence :
where is the set of all trees compatible with and evaluates the event that tree is the analysis of . Directly solving the equation is unfeasible because the number of possible trees grow exponentially with the length of the sentence. A common strategy is to factor each dependency tree into small parts which can be scored individually, then:
The order of a part is defined according to the number of dependencies it contains, a terminology used in previous parsing algorithms, such as first-order factorization and second-order sibling factorization. The factorizations in this new third-order parser uses the following parts:
Experimental results
Bla bla.
Related papers
Bla bla.
