Efficient algorithm for rational kernel evaluation in large lattice sets

Abstract

This paper presents an effective method for evaluation of the rational kernels represented by finite-state automata. The described algorithm is optimized for processing speed and thus facilitates the usage of state-of-the-art machine learning techniques like Support Vector Machines even in the real-time application of speech and language processing, such as dialogue systems and speech retrieval engines. The performance of the devised algorithm was tested on a spoken language understanding task and the results suggest that it consistently outperforms the baseline algorithm presented in the related literature.