Active learning has been successfully applied to a number of NLP tasks. In this paper, we present a study on Information Extraction for natural language licenses that need to be translated to RDF. The final purpose of our work is to automatically extract from a natural language document specifying a certain license a machine-readable description of the terms of use and reuse identified in such license. This task presents some peculiarities that make it specially interesting to study: highly repetitive text, few annotated or unannotated examples available, and very fine precision needed. In this paper we compare different active learning settings for this particular application. We show that the most straightforward approach to instance selection, uncertainty sampling, does not provide a good performance in this setting, performing even worse than passive learning. Density-based methods are the usual alternative to uncertainty sampling, in contexts with very few labelled instances. We show that we can obtain a similar effect to that of density-based methods using uncertainty sampling, by just reversing the ranking criterion, and choosing the most certain instead of the most uncertain instances.