Difference between revisions of "Headden et al. NAACL 09"
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This paper improves on unsupervised [[AddressesProblem::dependency parsing]] by introducing basic valence frames and lexical information. Smoothing is also performed to leverage on this additional information. Their model produces 10 percentage points improvements over previous work in unsupervised (dependency) grammar induction. | This paper improves on unsupervised [[AddressesProblem::dependency parsing]] by introducing basic valence frames and lexical information. Smoothing is also performed to leverage on this additional information. Their model produces 10 percentage points improvements over previous work in unsupervised (dependency) grammar induction. | ||
| − | The paper builds upon the [[RelatedPaper::Dependency Model with Valence]] by Klein and Manning (2004). | + | == Brief description of the method == |
| + | |||
| + | The paper builds upon the [[RelatedPaper::Dependency Model with Valence]] by Klein and Manning (2004). The DMV is a generative model in which the head of a sentence is generated and then each head recursively generates its left and right dependents. The arguments of the head in a certain direction are generated repeatedly by deciding whether to generate a new argument or to stop. | ||
| + | |||
| + | The dependency models used in the paper are framed in [[RelatedPaper::split-head bilexical CFGs]] (Eisner and Satta, 1999), which has a fast parsing algorithm to compute the expectations required by [[UsesMethod::Variational Bayes]]. | ||
| − | + | DMV models distributions over arguments identically without considering the order they are generated. The model used in the paper, EMV, distinguishes the distribution over the argument nearest to the head from the distribution of the subsequent argument. For instance, consider the phrase "the big dog", we would expect the distribution for the nearest argument "big" to be different from that of a further argument "the". | |
== Experimental Result == | == Experimental Result == | ||
Revision as of 17:20, 29 November 2011
Improving Unsupervised Dependency Parsing with Richer Contexts and Smoothing, by W. P. Headden III, W Headden III, M Johnson, D McClosky. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, 2009.
This Paper is available online [1].
Summary
This paper improves on unsupervised dependency parsing by introducing basic valence frames and lexical information. Smoothing is also performed to leverage on this additional information. Their model produces 10 percentage points improvements over previous work in unsupervised (dependency) grammar induction.
Brief description of the method
The paper builds upon the Dependency Model with Valence by Klein and Manning (2004). The DMV is a generative model in which the head of a sentence is generated and then each head recursively generates its left and right dependents. The arguments of the head in a certain direction are generated repeatedly by deciding whether to generate a new argument or to stop.
The dependency models used in the paper are framed in split-head bilexical CFGs (Eisner and Satta, 1999), which has a fast parsing algorithm to compute the expectations required by Variational Bayes.
DMV models distributions over arguments identically without considering the order they are generated. The model used in the paper, EMV, distinguishes the distribution over the argument nearest to the head from the distribution of the subsequent argument. For instance, consider the phrase "the big dog", we would expect the distribution for the nearest argument "big" to be different from that of a further argument "the".
Experimental Result
Related Papers
Corpus-Based Induction of Syntactic Structure: Models of Dependency and Constituency. D Klein and C Manning (2004). In ACL 2004
