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Previous research applying kernel methods to natural language parsing have focussed on proposing kernels over parse trees, which are hand-crafted based on domain knowledge and computational considerations. 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 parameters 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 reranking parse trees. . | Data-Defined Kernels for Parse Reranking Derived from Probabilistic Models James Henderson School of Informatics University of Edinburgh 2 Buccleuch Place Edinburgh EH8 9LW United Kingdom james.henderson@ed.ac.uk Ivan Titov Department of Computer Science University of Geneva 24 rue General Dufour CH-1211 Geneve 4 Switzerland ivan.titov@cui.unige.ch Abstract Previous research applying kernel methods to natural language parsing have focussed on proposing kernels over parse trees which are hand-crafted based on domain knowledge and computational considerations. 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 parameters 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 reranking parse trees. In experiments we use a neural network based statistical parser as the probabilistic model and use the resulting kernel with the Voted Perceptron algorithm to rerank the top 20 parses from the probabilistic model. This method achieves a significant improvement over the accuracy of the probabilistic model. 1 Introduction Kernel methods have been shown to be very effective in many machine learning problems. They have the advantage that learning can try to optimize measures related directly to expected testing performance i.e. large margin methods rather than the probabilistic measures used in statistical models which are only indirectly related to expected testing performance. Work on kernel methods in natural language has focussed on the definition of appropriate kernels for natural language tasks. In particular most of the work on parsing with kernel methods has focussed on kernels over parse trees Collins and Duffy 2002 Shen and Joshi 2003 Shen et al. 2003 Collins and Roark 2004 . These kernels have all been hand-crafted to try reflect properties of parse trees which are relevant to .
