222 citations · 311 across the 6 of their papers we have counts for
5 papers · 1 filter
Statistical Decision-Tree Models for Parsing
David M. Magerman
Syntactic natural language parsers have shown themselves to be inadequate for processing highly-ambiguous large-vocabulary text, as is evidenced by their poor performance on domain…
Natural Language Parsing as Statistical Pattern Recognition
David M. Magerman
Traditional natural language parsers are based on rewrite rule systems developed in an arduous, time-consuming manner by grammarians. A majority of the grammarian's efforts are dev…
Towards History-based Grammars: Using Richer Models for Probabilistic Parsing
Ezra Black, Fred Jelinek, John Lafferty +3
We describe a generative probabilistic model of natural language, which we call HBG, that takes advantage of detailed linguistic information to resolve ambiguity. HBG incorporates…
Efficiency, Robustness, and Accuracy in Picky Chart Parsing
David M. Magerman, Carl Weir
This paper describes Picky, a probabilistic agenda-based chart parsing algorithm which uses a technique called {\em probabilistic prediction} to predict which grammar rules are lik…
Pearl: A Probabilistic Chart Parser
David M. Magerman, Mitchell P. Marcus
This paper describes a natural language parsing algorithm for unrestricted text which uses a probability-based scoring function to select the "best" parse of a sentence. The parser…