Gaussian Process Pseudo-Likelihood Models for Sequence Labeling
This addresses sequence labeling problems in NLP, but appears incremental as it builds on existing Gaussian process and pseudo-likelihood methods.
The authors tackled sequence labeling in NLP by proposing Gaussian process models with pseudo-likelihood approximation to capture long-range dependencies without computational intractability, demonstrating usefulness through numerical experiments on datasets.
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.