On the Feasibility of Automatically Describing n-dimensional Objects

This paper introduces the problem of generating descriptions of n-dimensional spatial data by decomposing it via modelbased clustering. I apply the approach to the error function of supervised classification algorithms, a practical problem that uses Natural Language Generation for understanding the behaviour of a trained classifier. I demonstrate my system on a dataset taken from CoNLL shared tasks.