Work

Bayesian Kernel Methods for Natural Language Processing

Year: 2014

Type: conference-paper

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.

Source: Annual Meeting of the Association for Computational Linguistics: Student Research Workshop (ACL SRW)

DOI: 10.3115/v1/p14-3001

Author: Daniel Beck

Institution: University of Sheffield

Language: English

Topic: Gaussian Processes and Bayesian Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: European Commission, Conselho Nacional de Desenvolvimento Científico e Tecnológico, FP7 Information and Communication Technologies

Award: PREPARATION AND LAUNCH OF A LARGE-SCALE ACTION FOR QUALITY TRANSLATION TECHNOLOGY