Paper: Porting Statistical Parsers With Data-Defined Kernels

ACL ID W06-2902
Title Porting Statistical Parsers With Data-Defined Kernels
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2006

Previous results have shown disappointing performance when porting a parser trained on one domain to another domain where only a small amount of data is available. We propose the use of data-defined ker- nels as a way to exploit statistics from a source domain while still specializing a parser to a target domain. A probabilistic model trained on the source domain (and possibly also the target domain) is used to define a kernel, which is then used in a large margin classifier trained only on the target domain. With a SVM classifier and a neural network probabilistic model, this method achieves improved performance over the probabilistic model alone.