Difference between revisions of "Taskar et al. 2004. Max-margin Parsing"
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== Summary == | == Summary == | ||
| − | This paper presents a novel approach to parsing by maximizing separating margins using SVMs. They show how we can reformulate the parsing problem as a discriminative task, which | + | This paper presents a novel approach to parsing by maximizing separating margins using SVMs. They show how we can reformulate the parsing problem as a discriminative task, which allow an arbitrary number of features to be used. Also, such a formulation allows them to incorporate a loss function that directly penalizes incorrect parse trees appropriately. |
== Brief description of the method == | == Brief description of the method == | ||
| + | Instead of a probabilistic interpretation for parse trees, we seek to find: | ||
| + | |||
| + | <math>y_i==\arg\max_{y\in\mathbf{G}(x_i)} \langle\mathbf{w}, \Phi(x_i,y)\rangle</math> | ||
== Related Papers == | == Related Papers == | ||
In [[RelatedPaper::Bartlett et al NIPS 2004]], they used the EG algorithm for large margin structured classification. | In [[RelatedPaper::Bartlett et al NIPS 2004]], they used the EG algorithm for large margin structured classification. | ||
Revision as of 17:39, 30 October 2011
Max-margin parsing, by Ben Taskar, Taskar, B. and Klein, D. and Collins, M. and Koller, D. and Manning, C.. In Proc. EMNLP, 2004.
This Paper is available online [1].
Summary
This paper presents a novel approach to parsing by maximizing separating margins using SVMs. They show how we can reformulate the parsing problem as a discriminative task, which allow an arbitrary number of features to be used. Also, such a formulation allows them to incorporate a loss function that directly penalizes incorrect parse trees appropriately.
Brief description of the method
Instead of a probabilistic interpretation for parse trees, we seek to find:
Related Papers
In Bartlett et al NIPS 2004, they used the EG algorithm for large margin structured classification.
