  Here we proposed an approach to analyze text classification methods based on
the presence or absence of task-specific terms (and their synonyms) in the
text. We applied this approach to study six different transfer-learning and
unsupervised methods for screening articles relevant to COVID-19 vaccines and
therapeutics. The analysis revealed that while a BERT model trained on
search-engine results generally performed well, it miss-classified relevant
abstracts that did not contain task-specific terms. We used this insight to
create a more effective unsupervised ensemble.
