Bayesian nonparametric methods find solutions to tackle the automatic model-selection problem in statistics and machine learning. Such automatic model selection is critical because without this, users face the daunting task of manually finding the best algorithmic parameters. This work examined the joint modelling of both context (say location) and content (say text of webpages) of data in a Bayesian nonparametric setting. If this coupling can be performed in a principled way, we can effectively borrow the statistical strength provided by context to improve the inference of observations, or to extrapolate to new settings.