Paper: Hybrid Parsing: Using Probabilistic Models As Predictors For A Symbolic Parser

ACL ID P06-1041
Title Hybrid Parsing: Using Probabilistic Models As Predictors For A Symbolic Parser
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2006
Authors

In this paper we investigate the benefit of stochastic predictor components for the parsing quality which can be obtained with a rule-based dependency grammar. By in- cluding a chunker, a supertagger, a PP at- tacher, and a fast probabilistic parser we were able to improve upon the baseline by 3.2%, bringing the overall labelled accu- racy to 91.1% on the German NEGRA cor- pus. We attribute the successful integra- tion to the ability of the underlying gram- mar model to combine uncertain evidence in a soft manner, thus avoiding the prob- lem of error propagation.