# Gaussian Process Pseudo-Likelihood Models for Sequence Labeling

arXiv: https://arxiv.org/abs/1412.7868 · HTML: https://mlsanity.com/paper/1412.7868/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Dec 2014.

## Built on by (most cited first, 1 of 1)

- [Partial sequence labeling with structured Gaussian Processes](https://mlsanity.com/paper/2209.09397/) (2022)

## Abstract (arXiv)

Several machine learning problems arising in natural language processing can be modeled as a sequence labeling problem. We provide Gaussian process models based on pseudo-likelihood approximation to perform sequence labeling. Gaussian processes (GPs) provide a Bayesian approach to learning in a kernel based framework. The pseudo-likelihood model enables one to capture long range dependencies among the output components of the sequence without becoming computationally intractable. We use an efficient variational Gaussian approximation method to perform inference in the proposed model. We also provide an iterative algorithm which can effectively make use of the information from the neighboring labels to perform prediction. The ability to capture long range dependencies makes the proposed approach useful for a wide range of sequence labeling problems. Numerical experiments on some sequence labeling data sets demonstrate the usefulness of the proposed approach.