David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
We present a novel generative model for natural language tree structures in which semantic (lexical dependency) and syntactic (PCFG) structures are scored with separate models. This factorization provides conceptual simplicity, straightforward opportunities for separately improving the component models, and a level of performance comparable to similar, non-factored models. Most importantly, unlike other modern parsing models, the factored model admits an extremely effective A* parsing algorithm, which enables efﬁcient, exact inference.
|Keywords||No keywords specified (fix it)|
No categories specified
(categorize this paper)
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library||
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
Dan Klein & Christopher D. Manning, A Generative Constituent-Context Model for Improved Grammar Induction.
Christopher Manning, Incorporating Non-Local Information Into Information Extraction Systems by Gibbs Sampling.
Dan Klein & Christopher D. Manning, Parsing with Treebank Grammars: Empirical Bounds, Theoretical Models, and the Structure of the Penn Treebank.
Dan Klein & Christopher D. Manning, Natural Language Grammar Induction Using a Constituent-Context Model.
Christopher Manning, Verb Sense and Subcategorization: Using Joint Inference to Improve Performance on Complementary Tasks.
Added to index2010-12-22
Total downloads2 ( #343,262 of 1,098,129 )
Recent downloads (6 months)1 ( #283,807 of 1,098,129 )
How can I increase my downloads?