Fast Exact Inference with a Factored Model for Natural Language Parsing

Part of Advances in Neural Information Processing Systems 15 (NIPS 2002)

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Dan Klein, Christopher D. Manning


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 concep- tual simplicity, straightforward opportunities for separately improving the component models, and a level of performance comparable to simi- lar, non-factored models. Most importantly, unlike other modern parsing models, the factored model admits an extremely effective A* parsing al- gorithm, which enables ef´Čücient, exact inference.