Part of Advances in Neural Information Processing Systems 18 (NIPS 2005)
Jun Suzuki, Hideki Isozaki
This paper proposes a new approach to feature selection based on a sta- tistical feature mining technique for sequence and tree kernels. Since natural language data take discrete structures, convolution kernels, such as sequence and tree kernels, are advantageous for both the concept and accuracy of many natural language processing tasks. However, experi- ments have shown that the best results can only be achieved when lim- ited small sub-structures are dealt with by these kernels. This paper dis- cusses this issue of convolution kernels and then proposes a statistical feature selection that enable us to use larger sub-structures effectively. The proposed method, in order to execute efﬁciently, can be embedded into an original kernel calculation process by using sub-structure min- ing algorithms. Experiments on real NLP tasks conﬁrm the problem in the conventional method and compare the performance of a conventional method to that of the proposed method.