Paper: Data-Defined Kernels For Parse Reranking Derived From Probabilistic Models

ACL ID P05-1023
Title Data-Defined Kernels For Parse Reranking Derived From Probabilistic Models
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2005
Authors

Previous research applying kernel meth- ods to natural language parsing have fo- cussed on proposing kernels over parse trees, which are hand-crafted based on do- main knowledge and computational con- siderations. In this paper we propose a method for defining kernels in terms of a probabilistic model of parsing. This model is then trained, so that the param- eters of the probabilistic model reflect the generalizations in the training data. The method we propose then uses these trained parameters to define a kernel for rerank- ing parse trees. In experiments, we use a neural network based statistical parser as the probabilistic model, and use the resulting kernel with the Voted Percep- tron algorithm to rerank the top 20 parses from the probabilistic model. This method achieves a significa...