Difference between revisions of "Koo and Collins ACL 2010"
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This [[Category::paper]] presents a higher-order [[AddressesProblem::Dependency Parsing|dependency parser]] that can evaluate substructures containing three dependencies, using both sibling-style and grandchild-style interactions. The algorithms presented require only <math>O(n^4)</math> time and were evaluated on the [[UsesDataset::Penn Treebank]] and the [[UsesDataset::Prague Dependency Treebank]]. The implementation code was publicly released [http://groups.csail.mit.edu/nlp/dpo3/]. | This [[Category::paper]] presents a higher-order [[AddressesProblem::Dependency Parsing|dependency parser]] that can evaluate substructures containing three dependencies, using both sibling-style and grandchild-style interactions. The algorithms presented require only <math>O(n^4)</math> time and were evaluated on the [[UsesDataset::Penn Treebank]] and the [[UsesDataset::Prague Dependency Treebank]]. The implementation code was publicly released [http://groups.csail.mit.edu/nlp/dpo3/]. | ||
| − | Dependency parsing is defined as a search for the highest-scoring analysis of <math> x </math>: | + | Dependency parsing is defined as a search for the highest-scoring analysis of <math>\, x</math>: |
<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> | ||
| − | 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 sentence <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 sentence <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: |
<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> | ||
Revision as of 22:24, 25 November 2011
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].
Dependency parsing is defined as a search for the highest-scoring analysis of :
Where is the set of all trees compatible with and evaluates the event that tree is the analysis of sentence . 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:
Experimental results
Bla bla.
Related papers
Bla bla.
