Exploring Syntactic Features for Pronoun Resolution Using Context-Sensitive Convolution Tree Kernel

This paper proposes to use a convolution kernel over parse tree to model syntactic structure information for pronoun resolution. Our study reveals that the syntactic structure features embedded in a parse tree are very effective for pronoun resolution and these features can be well captured by the context-sensitive convolution tree kernel. Evaluation on the ACE 2003 corpus shows that among all structured syntactic feature space, Shortest Path Tree achieves the best performance. Then we incorporate more features into SPT, result shows that SPT can use successfully with normal features. Finally, we compare our system with other pronoun resolution systems, our results are outstanding in success rate than normal features and tree kernel-based method of Yang.