Bayesian Kernel Methods for Natural Language Processing
Daniel Beck · 2014
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.