  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.
