Fast Exact Inference with a Factored Model for Natural Language Parsing

Dan Klein; Christopher D. Manning

Abstract

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

Computer scienceParsingArtificial intelligenceInferenceNatural language processingDependency (UML)Bottom-up parsingDependency grammarGenerative grammarSimplicityTop-down parsingNatural languageParser combinatorComponent (thermodynamics)Top-down parsing languageGenerative modelTree (set theory)Programming languageMathematics

Affiliated Institutions

Stanford University US

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Publication Info

Year: 2002
Type: article
Volume: 15
Pages: 3-10
Citations: 745
Access: Closed

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APA Style

                            
                                    Dan Klein, 
                                
                                    Christopher D. Manning
                                
                            (2002). 
                            Fast Exact Inference with a Factored Model for Natural Language Parsing. 
                            
                            , 15
                            
                            , 3-10.