  Concept tagging is a type of structured learning needed for natural language
understanding (NLU) systems. In this task, meaning labels from a domain
ontology are assigned to word sequences. In this paper, we review the
algorithms developed over the last twenty five years. We perform a comparative
evaluation of generative, discriminative and deep learning methods on two
public datasets. We report on the statistical variability performance
measurements. The third contribution is the release of a repository of the
algorithms, datasets and recipes for NLU evaluation.
