Paper: Bayesian Kernel Methods for Natural Language Processing

ACL ID P14-3001
Title Bayesian Kernel Methods for Natural Language Processing
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

Kernel methods are heavily used in Natu- ral Language Processing (NLP). Frequen- tist approaches like Support Vector Ma- chines 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.