  Large amounts of annotated data have become more important than ever,
especially since the rise of deep learning techniques. However, manual
annotations are costly. We propose a tool that enables researchers to create
large, high-quality, annotated datasets with only a few manual annotations,
thus strongly reducing annotation cost and effort. For this purpose, we combine
an active learning (AL) approach with a pre-trained language model to
semi-automatically identify annotation categories in the given text documents.
To highlight our research direction's potential, we evaluate the approach on
the task of identifying frames in news articles. Our preliminary results show
that employing AL strongly reduces the number of annotations for correct
classification of even these complex and subtle frames. On the framing dataset,
the AL approach needs only 16.3\% of the annotations to reach the same
performance as a model trained on the full dataset.
