Towards History-based Grammars: Using Richer Models for Probabilistic Parsing 
 
 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 lexical, syntactic, semantic, and structural information from the parse tree into the disambiguation process in a novel way. We use a corpus of bracketed sentences , called a Treebank , in combination with decision tree building to tease out the relevant aspects of a parse tree that will determine the correct parse of a sentence . This stands in contrast to the usual approach of further grammar tailoring via the usual linguistic introspection in the hope of generating the correct parse . In head-to-head tests against one of the best existing robust probabilistic parsing models , which we call P-CFG, the HBG model significantly outperforms P-CFG , increasing the parsing accuracy rate from 60% to 75%, a 37% reduction in error.  