Fast Exact Inference with a Factored Model for Natural Language Parsing

Fast Exact Inference with a Factored Model for Natural Language Parsing (2003)

by Dan Klein , Christopher D. Manning

Venue:	In Advances in Neural Information Processing Systems 15 (NIPS
Citations:	145 - 7 self

Datum	Value	Source
TITLE	Fast exact inference with a factored model for natural language parsing	INFERENCE
AUTHOR NAME	Dan Klein	SVM HeaderParse 0.1
AUTHOR AFFIL	Department of Computer Science; Stanford University	SVM HeaderParse 0.2
AUTHOR ADDR	Stanford, CA 94305-9040	SVM HeaderParse 0.1
AUTHOR NAME	Christopher D. Manning	SVM HeaderParse 0.1
AUTHOR AFFIL	Department of Computer Science; Stanford University	SVM HeaderParse 0.2
AUTHOR ADDR	Stanford, CA 94305-9040	SVM HeaderParse 0.1
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. 1	SVM HeaderParse 0.1
YEAR	2003	INFERENCE
VENUE	In Advances in Neural Information Processing Systems	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
VOLUME	15	INFERENCE
CITATIONS	18 found	ParsCit 1.0