  Manually labelling large collections of text data is a time-consuming,
expensive, and laborious task, but one that is necessary to support machine
learning based on text datasets. Active learning has been shown to be an
effective way to alleviate some of the effort required in utilising large
collections of unlabelled data for machine learning tasks without needing to
fully label them. The representation mechanism used to represent text documents
when performing active learning, however, has a significant influence on how
effective the process will be. While simple vector representations such as bag
of words have been shown to be an effective way to represent documents during
active learning, the emergence of representation mechanisms based on the word
embeddings prevalent in neural network research (e.g. word2vec and
transformer-based models like BERT) offer a promising, and as yet not fully
explored, alternative. This paper describes a large-scale evaluation of the
effectiveness of different text representation mechanisms for active learning
across 8 datasets from varied domains. This evaluation shows that using
representations based on modern word embeddings---especially BERT---, which
have not yet been widely used in active learning, achieves a significant
improvement over more commonly used vector-based methods like bag of words.
