A tool that enables the use of active learning, as well as the incorporation of word embeddings, was evaluated for its ability to decrease the training data set size required for a named entity recognition model. Uncertainty-based active learning and the use of word embeddings led to very large performance improvements on small data sets for the entity categories PERSON and LOCATION. In contrast, the embedding features used were shown to be unsuitable for detecting entities belonging to the ORGANISATION category. The tool was also extended with functionality for visualising the usefulness of the active learning process and of the word embeddings used. The visualisations provided were able to indicate the performance differences between the entities, as well as differences with regards to usefulness of the embedding features.