  Labeling data can be an expensive task as it is usually performed manually by
domain experts. This is cumbersome for deep learning, as it is dependent on
large labeled datasets. Active learning (AL) is a paradigm that aims to reduce
labeling effort by only using the data which the used model deems most
informative. Little research has been done on AL in a text classification
setting and next to none has involved the more recent, state-of-the-art Natural
Language Processing (NLP) models. Here, we present an empirical study that
compares different uncertainty-based algorithms with BERT$_{base}$ as the used
classifier. We evaluate the algorithms on two NLP classification datasets:
Stanford Sentiment Treebank and KvK-Frontpages. Additionally, we explore
heuristics that aim to solve presupposed problems of uncertainty-based AL;
namely, that it is unscalable and that it is prone to selecting outliers.
Furthermore, we explore the influence of the query-pool size on the performance
of AL. Whereas it was found that the proposed heuristics for AL did not improve
performance of AL; our results show that using uncertainty-based AL with
BERT$_{base}$ outperforms random sampling of data. This difference in
performance can decrease as the query-pool size gets larger.
