Bayesian Kernel Methods for Natural Language Processing

Abstract

Kernel methods are heavily used in Natural Language Processing (NLP). Frequentist approaches like Support Vector Machines are the state-of-the-art in many tasks. However, these approaches lack efficient procedures for model selection, which hinders the usage of more advanced kernels. In this work, we propose the use of a Bayesian approach for kernel methods, Gaussian Processes, which allow easy model fitting even for complex kernel combinations. Our goal is to employ this approach to improve results in a number of regression and classification tasks in NLP.