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

COVID-19: Comparative Analysis of Methods for Identifying Articles Related to Therapeutics and Vaccines without Using Labeled Data