Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing
Table 3. Datasets used in the BLURB biomedical NLP benchmark. We list the numbers of instances in train, dev, and test (e.g., entity mentions in NER and PICO elements in evidence-based medical information extraction).
['1pt1.5pt1.5pt Dataset', 'Task', 'Train', 'Dev', 'Test', 'Evaluation Metrics']
BLURB is comprised of a comprehensive set of biomedical NLP tasks from publicly available datasets, including named entity recognition (NER), evidence-based medical information extraction (PICO), relation extraction, sentence similarity, document classification, and question answering. The latter types often require additional engineering effort that are not relevant to evaluating neural language models. For simplicity, we focus on the classification tasks such as yes/ no question-answering in BLURB, and leave the inclusion of more complex question-answering to future work.